CN113807530A - Information processing system, method and device - Google Patents
Information processing system, method and device Download PDFInfo
- Publication number
- CN113807530A CN113807530A CN202011018697.7A CN202011018697A CN113807530A CN 113807530 A CN113807530 A CN 113807530A CN 202011018697 A CN202011018697 A CN 202011018697A CN 113807530 A CN113807530 A CN 113807530A
- Authority
- CN
- China
- Prior art keywords
- encryption
- processed
- leaf node
- leaf nodes
- tree model
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 230000010365 information processing Effects 0.000 title claims abstract description 63
- 238000000034 method Methods 0.000 title claims abstract description 49
- 230000004044 response Effects 0.000 claims abstract description 76
- 238000012545 processing Methods 0.000 claims abstract description 73
- 238000012216 screening Methods 0.000 claims abstract description 39
- 238000003672 processing method Methods 0.000 claims description 14
- 238000004590 computer program Methods 0.000 claims description 9
- 238000010586 diagram Methods 0.000 description 15
- 230000008569 process Effects 0.000 description 13
- 238000012549 training Methods 0.000 description 10
- 230000006870 function Effects 0.000 description 6
- 238000004891 communication Methods 0.000 description 5
- 238000005516 engineering process Methods 0.000 description 5
- 230000003287 optical effect Effects 0.000 description 4
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 2
- 230000000644 propagated effect Effects 0.000 description 2
- 230000000694 effects Effects 0.000 description 1
- 230000008030 elimination Effects 0.000 description 1
- 238000003379 elimination reaction Methods 0.000 description 1
- 239000000835 fiber Substances 0.000 description 1
- 239000004973 liquid crystal related substance Substances 0.000 description 1
- 238000010801 machine learning Methods 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 238000007637 random forest analysis Methods 0.000 description 1
- 239000004065 semiconductor Substances 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/243—Classification techniques relating to the number of classes
- G06F18/24323—Tree-organised classifiers
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
- G06F21/60—Protecting data
- G06F21/62—Protecting access to data via a platform, e.g. using keys or access control rules
- G06F21/6218—Protecting access to data via a platform, e.g. using keys or access control rules to a system of files or objects, e.g. local or distributed file system or database
- G06F21/6245—Protecting personal data, e.g. for financial or medical purposes
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Data Mining & Analysis (AREA)
- Software Systems (AREA)
- General Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Evolutionary Computation (AREA)
- General Health & Medical Sciences (AREA)
- Bioethics (AREA)
- Health & Medical Sciences (AREA)
- Artificial Intelligence (AREA)
- Medical Informatics (AREA)
- Mathematical Physics (AREA)
- Computing Systems (AREA)
- Life Sciences & Earth Sciences (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Evolutionary Biology (AREA)
- Databases & Information Systems (AREA)
- Computer Hardware Design (AREA)
- Computer Security & Cryptography (AREA)
- Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
Abstract
The embodiment of the disclosure discloses an information processing system, method and device. One embodiment of the system comprises: a first end configured to: screening out target leaf nodes which cannot be reached by the object to be processed based on the first characteristic information of the object to be processed; for leaf nodes included in the tree model, responding to the leaf nodes as target leaf nodes, encrypting 0 to obtain encryption weight; in response to the leaf node not being the target leaf node, encrypting the weight of the leaf node to obtain an encryption weight; sending the encryption weight and a processing request aiming at the object to be processed to a second end; a second end configured to: screening out at least two candidate leaf nodes which are possibly reached by the object to be processed based on the second characteristic information of the object to be processed; summing the encryption weights of at least two candidate leaf nodes to obtain an encryption score; the encrypted results are sent to the first end. This embodiment may improve the security of the first-end data.
Description
Technical Field
Embodiments of the present disclosure relate to the field of computer technologies, and in particular, to an information processing system, method, and apparatus.
Background
In machine learning, data determines the upper limit of model effect, and in order to further improve the accuracy of the model, a business side can aggregate data of a data side to carry out federal learning of the model. The business party may be a party that provides both the feature data and the label of the feature data during the process of training the model, and the data party may be a party that provides only the feature data.
In practice, the model obtained by training can be deployed on the business side and the data side respectively, but the business side and the data side only manage partial features and feature thresholds of the model.
Because the business side only manages partial characteristics and characteristic threshold values of the model, when the model is used for prediction, the business side also needs to cooperate with the data side to complete the prediction process.
Disclosure of Invention
The embodiment of the disclosure provides an information processing system, method and device.
In a first aspect, an embodiment of the present disclosure provides an information processing system, which includes a first end and a second end, the first end and the second end having the same tree model, the first end and the second end respectively managing part of nodes in the tree model, the first end having weights of leaf nodes of the tree model, wherein the first end is configured to: acquiring first characteristic information of an object to be processed; based on the first characteristic information, leaf nodes which cannot be reached by the object to be processed are screened out from the leaf nodes included in the tree model and serve as target leaf nodes; for leaf nodes included in the tree model, the following processing steps are performed: in response to the leaf node being the target leaf node, encrypting 0 to obtain an encryption weight corresponding to the target leaf node; in response to that the leaf node is not the target leaf node, encrypting the weight of the leaf node to obtain an encryption weight corresponding to the leaf node; sending the obtained encryption weight and a processing request aiming at the object to be processed to a second end; the second end is configured to: acquiring second characteristic information of the object to be processed in response to the received encryption weight sent by the first end and the processing request aiming at the object to be processed; screening at least two leaf nodes which are possibly reached by the object to be processed from the leaf nodes included in the tree model based on the second characteristic information to serve as at least two candidate leaf nodes, wherein the at least two candidate leaf nodes include a target leaf node; summing the encryption weights corresponding to the at least two candidate leaf nodes to obtain the encryption score of the object to be processed in the tree model; the encrypted results are sent to the first end.
In some embodiments, the tree model includes at least two trees; and the second end is further configured to: for a tree of the at least two trees comprised by the tree model, performing the following steps: screening at least two leaf nodes which are possibly reached by an object to be processed from the leaf nodes included in the tree as at least two candidate leaf nodes; summing the encryption weights corresponding to at least two candidate leaf nodes of the tree to obtain an initial encryption score corresponding to the tree; and summing at least two initial encryption scores corresponding to at least two trees included in the tree model to obtain the encryption score of the object to be processed in the tree model.
In some embodiments, the first end is further configured to: decrypting the received encrypted score to obtain a target score; and classifying the object to be processed based on the target score.
In some embodiments, the first end is further configured to: in response to that the leaf node is a target leaf node, encrypting 0 by using a homomorphic encryption mode to obtain an encryption weight corresponding to the target leaf node; and in response to that the leaf node is not the target leaf node, encrypting the weight of the leaf node by using a homomorphic encryption mode to obtain the encryption weight corresponding to the leaf node. In a second aspect, an embodiment of the present disclosure provides an information processing method performed by a first peer in an information processing system, the information processing system including the first peer and a second peer, the first peer and the second peer having a same tree model, the first peer and the second peer managing part of nodes in the tree model, respectively, the first peer having weights of leaf nodes of the tree model, the method including: acquiring first characteristic information of an object to be processed; based on the first characteristic information, leaf nodes which cannot be reached by the object to be processed are screened out from the leaf nodes included in the tree model and serve as target leaf nodes; for leaf nodes included in the tree model, the following processing steps are performed: in response to the leaf node being the target leaf node, encrypting 0 to obtain an encryption weight corresponding to the target leaf node; in response to that the leaf node is not the target leaf node, encrypting the weight of the leaf node to obtain an encryption weight corresponding to the leaf node; sending the obtained encryption weight and a processing request aiming at the object to be processed to a second end; and receiving the encryption score from the second end, wherein the target encryption score is obtained by the second end by: acquiring second characteristic information of the object to be processed; screening at least two leaf nodes which are possibly reached by the object to be processed from the leaf nodes included in the tree model based on the second characteristic information to serve as at least two candidate leaf nodes, wherein the at least two candidate leaf nodes include a target leaf node; and summing the encryption weights corresponding to the at least two candidate leaf nodes to obtain the encryption score of the object to be processed in the tree model.
In some embodiments, the method further comprises: decrypting the received encrypted score to obtain a target score; and classifying the object to be processed based on the target score.
In a third aspect, an embodiment of the present disclosure provides an information processing method, performed by a second end in an information processing system, the information processing system including a first end and a second end, the first end and the second end having a same tree model, the first end and the second end managing part of nodes in the tree model, respectively, the first end having weights of leaf nodes of the tree model, the method including: in response to receiving the encryption weight sent by the first end and the processing request aiming at the object to be processed, acquiring second characteristic information of the object to be processed, wherein the encryption weight is obtained by the first end through the following steps: acquiring first characteristic information of an object to be processed; based on the first characteristic information, leaf nodes which cannot be reached by the object to be processed are screened out from the leaf nodes included in the tree model and serve as target leaf nodes; for leaf nodes included in the tree model, the following processing steps are performed: in response to the leaf node being the target leaf node, encrypting 0 to obtain an encryption weight corresponding to the target leaf node; in response to that the leaf node is not the target leaf node, encrypting the weight of the leaf node to obtain an encryption weight corresponding to the leaf node; screening at least two leaf nodes which are possibly reached by the object to be processed from the leaf nodes included in the tree model based on the second characteristic information to serve as at least two candidate leaf nodes, wherein the at least two candidate leaf nodes include a target leaf node; summing the encryption weights corresponding to the at least two candidate leaf nodes to obtain the encryption score of the object to be processed in the tree model; the encrypted results are sent to the first end.
In some embodiments, the tree model includes at least two trees; screening at least two leaf nodes which are possibly reached by the object to be processed from the leaf nodes included in the tree model based on the second characteristic information to serve as at least two candidate leaf nodes; summing the encryption weights corresponding to the at least two candidate leaf nodes to obtain an encryption score of the object to be processed in the tree model comprises: for a tree of the at least two trees comprised by the tree model, performing the following steps: screening at least two leaf nodes which are possibly reached by an object to be processed from the leaf nodes included in the tree as at least two candidate leaf nodes; summing the encryption weights corresponding to at least two candidate leaf nodes of the tree to obtain an initial encryption score corresponding to the tree; and summing at least two initial encryption scores corresponding to at least two trees included in the tree model to obtain the encryption score of the object to be processed in the tree model.
In a fourth aspect, an embodiment of the present disclosure provides an information processing apparatus configured at a first end in an information processing system, the information processing system including the first end and a second end, the first end and the second end having a same tree model, the first end and the second end managing part of nodes in the tree model, respectively, the first end having weights of leaf nodes of the tree model, the apparatus including: a first acquisition unit configured to acquire first feature information of an object to be processed; a first screening unit configured to screen leaf nodes, which are not reached by the object to be processed, from the leaf nodes included in the tree model as target leaf nodes based on the first feature information; an execution unit configured to execute the following processing steps for leaf nodes included in the tree model: in response to the leaf node being the target leaf node, encrypting 0 to obtain an encryption weight corresponding to the target leaf node; in response to that the leaf node is not the target leaf node, encrypting the weight of the leaf node to obtain an encryption weight corresponding to the leaf node; a first transmission unit configured to transmit the obtained encryption weight and a processing request for the object to be processed to the second end; and a receiving unit configured to receive the encryption score from the second end, wherein the target encryption score is obtained by the second end by: acquiring second characteristic information of the object to be processed; screening at least two leaf nodes which are possibly reached by the object to be processed from the leaf nodes included in the tree model based on the second characteristic information to serve as at least two candidate leaf nodes, wherein the at least two candidate leaf nodes include a target leaf node; and summing the encryption weights corresponding to the at least two candidate leaf nodes to obtain the encryption score of the object to be processed in the tree model.
In some embodiments, the apparatus further comprises: a decryption unit configured to decrypt the received encrypted score to obtain a target score; a classification unit configured to classify the object to be processed based on the target score.
In some embodiments, the execution unit is further configured to: in response to that the leaf node is a target leaf node, encrypting 0 by using a homomorphic encryption mode to obtain an encryption weight corresponding to the target leaf node; and in response to that the leaf node is not the target leaf node, encrypting the weight of the leaf node by using a homomorphic encryption mode to obtain the encryption weight corresponding to the leaf node. In a fifth aspect, an embodiment of the present disclosure provides an information processing apparatus configured at a second end in an information processing system, the information processing system including a first end and a second end, the first end and the second end having a same tree model, the first end and the second end managing part of nodes in the tree model, respectively, the first end having weights of leaf nodes of the tree model, the apparatus including: a second obtaining unit configured to obtain second feature information of the object to be processed in response to receiving the encryption weight and the processing request for the object to be processed, the encryption weight being obtained by the first end by: acquiring first characteristic information of an object to be processed; based on the first characteristic information, leaf nodes which cannot be reached by the object to be processed are screened out from the leaf nodes included in the tree model and serve as target leaf nodes; for leaf nodes included in the tree model, the following processing steps are performed: in response to the leaf node being the target leaf node, encrypting 0 to obtain an encryption weight corresponding to the target leaf node; in response to that the leaf node is not the target leaf node, encrypting the weight of the leaf node to obtain an encryption weight corresponding to the leaf node; a second screening unit configured to screen, from the leaf nodes included in the tree model, at least two leaf nodes that may be reached by the object to be processed as at least two candidate leaf nodes based on the second feature information, where the at least two candidate leaf nodes include a target leaf node; the summing unit is configured to sum the encryption weights corresponding to the at least two candidate leaf nodes to obtain an encryption score of the object to be processed in the tree model; a second transmitting unit configured to transmit the encrypted result to the first terminal.
In some embodiments, the tree model includes at least two trees; and the second screening unit and summing unit are further configured to: for a tree of the at least two trees comprised by the tree model, performing the following steps: screening at least two leaf nodes which are possibly reached by an object to be processed from the leaf nodes included in the tree as at least two candidate leaf nodes; summing the encryption weights corresponding to at least two candidate leaf nodes of the tree to obtain an initial encryption score corresponding to the tree; and summing at least two initial encryption scores corresponding to at least two trees included in the tree model to obtain the encryption score of the object to be processed in the tree model.
In a sixth aspect, an embodiment of the present disclosure provides an electronic device, including: one or more processors; a storage device having one or more programs stored thereon, which when executed by one or more processors, cause the one or more processors to implement the method of any of the embodiments of the information processing method described above.
In a seventh aspect, embodiments of the present disclosure provide a computer-readable medium on which a computer program is stored, which when executed by a processor, implements the method of any of the above-described information processing methods.
In practice, model interpretability is of great importance in many fields. The interpretable model may provide a basis for it to obtain a certain decision. Currently, in the field of federal learning, in order to improve model interpretability, a business party may disclose features corresponding to nodes that it manages to a data party. However, in the process of prediction by using the model, the service party usually discloses the splitting direction of the object to be predicted at the node managed by the service party to the data party, so that the data party can simultaneously obtain the feature corresponding to the node managed by the service party and the splitting direction of the object to be predicted under the feature, which may cause the data party to conjecture the feature value corresponding to the node managed by the service party based on the splitting direction and the feature, in combination with background knowledge or an elimination method, and the like, thereby causing the privacy of the service party to be leaked.
The information processing system, method and device provided by the embodiments of the present disclosure may acquire first feature information of an object to be processed through a first end, then, based on the first feature information, screen out leaf nodes, which are not reached by the object to be processed, from the leaf nodes included in a tree model as target leaf nodes, and then, for the leaf nodes included in the tree model, perform the following processing steps: in response to the leaf node being the target leaf node, encrypting 0 to obtain an encryption weight corresponding to the target leaf node; in response to the fact that the leaf node is not the target leaf node, the weight of the leaf node is encrypted to obtain an encryption weight corresponding to the leaf node, the obtained encryption weight and a processing request for the object to be processed are sent to a second end, the second end can obtain second feature information of the object to be processed in response to the fact that the encryption weight sent by the first end and the processing request for the object to be processed are received, then at least two leaf nodes which are possibly reached by the object to be processed are selected from the leaf nodes included in the tree model based on the second feature information to serve as at least two candidate leaf nodes, wherein the at least two candidate leaf nodes include the target leaf node, then the encryption weights corresponding to the at least two candidate leaf nodes are summed to obtain an encryption score of the object to be processed in the tree model, and finally the encryption score is sent to the first end, therefore, the first end can encrypt the virtual weight (namely 0) of the target leaf node and the real weight of the leaf node except the target leaf node respectively and then send the encrypted virtual weight and the real weight to the second end, the encryption technology can ensure that the second end cannot distinguish the target leaf node and further cannot know the splitting direction of the first end, and the encryption of 0 can ensure that the contribution degree of the target leaf node to the score of the object to be processed is 0, so that the accuracy of the score of the object to be processed obtained through summation can be ensured, therefore, the privacy of the first end can be protected under the condition that the first end can obtain the accurate score of the object to be processed, and the safety of data of the first end is improved; in addition, the second end in the disclosure only needs to distribute the encrypted result obtained by summing to the first end, and does not need to disclose the splitting direction to the first end, so the disclosure can also protect the privacy of the second end.
Drawings
Other features, objects and advantages of the disclosure will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:
FIG. 1 is an exemplary system architecture diagram of an information handling system of the present disclosure;
FIG. 2 is a timing diagram of an embodiment of an information handling system according to the present disclosure;
FIG. 3 is a schematic diagram of the structure of a tree model in one embodiment of a system for processing information according to the present disclosure;
FIG. 4 is a flow diagram for one embodiment of an information processing method according to the present disclosure;
FIG. 5 is a flow diagram of yet another embodiment of an information processing method according to the present disclosure;
FIG. 6 is a schematic block diagram of one embodiment of an information processing apparatus according to the present disclosure;
fig. 7 is a schematic configuration diagram of still another embodiment of an information processing apparatus according to the present disclosure;
FIG. 8 is a schematic structural diagram of a computer system suitable for use with the electronic device used to implement embodiments of the present disclosure.
Detailed Description
The present disclosure is described in further detail below with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the related invention are shown in the drawings.
It should be noted that, in the present disclosure, the embodiments and features of the embodiments may be combined with each other without conflict. The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
FIG. 1 illustrates an exemplary system architecture 100 of the information handling system of the present disclosure.
As shown in fig. 1, the system architecture 100 may include first ends 101, 102, a network 103, and second ends 104, 105. The network 103 serves as a medium for providing communication links between the first ends 101, 102 and the second ends 104, 105. Network 103 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The first ends 101, 102 and the second ends 104, 105 may be participating ends of federal learning of the model. Specifically, the first ends 101 and 102 may correspond to business parties in the federal learning process, and the second ends 104 and 105 may correspond to data parties in the federal learning process. Wherein, the business side can be a side which provides the feature data and the label of the feature data simultaneously in the process of federal learning. The data party may be the party that only provides the characteristic data. The dimensions of the feature data provided by the business party are typically different from the dimensions of the data provided by the data party (e.g., the business party provides feature data for an age feature, and the data party provides feature data for a height feature). The models obtained after the federal learning can be deployed in the first terminals 101, 102 and the second terminals 104, 105, respectively. The first terminals 101, 102 and the second terminals 104, 105 respectively manage the characteristics corresponding to the characteristic data provided by them.
The first terminals 101, 102 may be hardware or software. When the first end 101, 102 is hardware, it can be a variety of electronic devices including, but not limited to, servers, smart phones, tablets, laptop and desktop computers, and the like. When the first end 101, 102 is software, it can be installed in the electronic devices listed above. It may be implemented as multiple pieces of software or software modules (e.g., multiple pieces of software or software modules to provide distributed services) or as a single piece of software or software module. And is not particularly limited herein.
Similarly, the second terminals 104, 105 may be hardware or software. When the second end 104, 105 is hardware, it can be various electronic devices including, but not limited to, a server, a smart phone, a tablet computer, a laptop portable computer, a desktop computer, and the like. When the second terminal 104, 105 is software, it can be installed in the electronic devices listed above. It may be implemented as multiple pieces of software or software modules (e.g., multiple pieces of software or software modules to provide distributed services) or as a single piece of software or software module. And is not particularly limited herein.
It should be understood that the number of first ends, networks and second ends in fig. 1 is merely illustrative. There may be any number of first ends, networks, and second ends, as desired for the implementation.
Referring to FIG. 2, a timing diagram 200 of one embodiment of an information handling system according to the present application is shown.
The information processing system in the embodiment of the application may include a first end and a second end, the first end and the second end may have the same tree model, and the tree model may be various models composed of trees, for example, a gradient lifting tree model, a random forest, and the like. Specifically, the tree model may be a model obtained by the first end and the second end through joint learning training. The first and second ends may manage a portion of the nodes in the tree model, respectively.
Specifically, as an example, please refer to fig. 3, and fig. 3 shows a schematic structural diagram of a tree model according to the present embodiment. The tree model may be configured at a first end and a second end, respectively. In particular, the tree model may include three nodes 301, 302, and 303, and four leaf nodes 304, 305, 306, and 307. The node 301 of the three nodes may be a node managed by a first end, and the node 302 and the node 303 may be nodes managed by a second end.
In this embodiment, the nodes in the tree model managed by the first end and the second end respectively may be determined in the process of training to obtain the tree model. Specifically, the nodes managed by the first end and the second end are related to the feature information that the first end and the second end can acquire. For example, if the first peer can obtain age information and the second peer can obtain height information, the first peer manages nodes corresponding to age characteristics in the tree model and the second peer manages nodes corresponding to height characteristics in the tree model.
In this embodiment, the first end may have weights of leaf nodes of the tree model. Here, the nodes of the tree model may be split, while the leaf nodes of the tree model may not be split. The weights of the leaf nodes may be obtained during the training of the tree model. In particular, the weight of a leaf node may be used to indicate the number of training objects in the set of training objects that fall on the leaf node. The greater the number, the greater the weight of the leaf node may be.
It should be noted that the method for obtaining the tree model through the joint learning training is a well-known technology which is widely researched and applied at present, and is not described herein again.
In this embodiment, the first end may be configured to: acquiring first characteristic information of an object to be processed; based on the first characteristic information, leaf nodes which cannot be reached by the object to be processed are screened out from the leaf nodes included in the tree model and serve as target leaf nodes; for leaf nodes included in the tree model, the following processing steps are performed: in response to the leaf node being the target leaf node, encrypting 0 to obtain an encryption weight corresponding to the target leaf node; in response to that the leaf node is not the target leaf node, encrypting the weight of the leaf node to obtain an encryption weight corresponding to the leaf node; and sending the obtained encryption weight and the processing request aiming at the object to be processed to the second end. The second end may be configured to: acquiring second characteristic information of the object to be processed in response to the received encryption weight sent by the first end and the processing request aiming at the object to be processed; screening at least two leaf nodes which are possibly reached by the object to be processed from the leaf nodes included in the tree model based on the second characteristic information to serve as at least two candidate leaf nodes, wherein the at least two candidate leaf nodes include a target leaf node; summing the encryption weights corresponding to the at least two candidate leaf nodes to obtain the encryption score of the object to be processed in the tree model; the encrypted results are sent to the first end.
Specifically, as shown in fig. 2, in step 201, the first end acquires first feature information of the object to be processed.
In this embodiment, the first end (for example, the first end 101 shown in fig. 1) may obtain the first characteristic information of the object to be processed from a remote location or a local location by a wired connection manner or a wireless connection manner. The object to be processed may be an object to be processed, and specifically, may be various objects that can be processed by the tree model, for example, an image, a text, a video, an audio, and the like. The first characteristic information of the object to be processed may be used to characterize the characteristic of the object to be processed. Specifically, the features represented by the first feature information acquired by the first end may be the same as the features corresponding to the nodes managed by the first end.
As an example, if the feature corresponding to the node managed by the first end is an age feature, the first feature information may be information for characterizing the age feature of the object to be processed (e.g., a user).
In step 202, the first end screens out leaf nodes, which are not reached by the object to be processed, from the leaf nodes included in the tree model as target leaf nodes based on the first feature information.
In this embodiment, based on the first characteristic information obtained in step 201, the first end may split the object to be processed in the tree model, so as to screen out leaf nodes that are not reached by the object to be processed from the leaf nodes included in the tree model as target leaf nodes.
Specifically, the first end may determine, based on the first feature information, a leaf node that the object to be processed may reach and a target leaf node from leaf nodes included in the tree model, where the target leaf node is a leaf node in the tree model except for the leaf node that the object to be processed may reach.
It will be appreciated that there are first end managed nodes and second end managed nodes in the tree model. For a node managed by the first end in the tree model, the first end may determine, based on the first feature information, a splitting direction of the object to be processed at the node, and may further determine, according to the splitting direction, a target leaf node that the object to be processed does not reach. In particular, the first end may further determine a leaf node that can be reached by the object to be processed according to the splitting direction. For a node managed by the second end in the tree model, the first end cannot know the splitting direction of the object to be processed at the node, and in this case, the first end can use each splitting direction of the object to be processed at the node as a possible splitting direction, and further use leaf nodes corresponding to the node as leaf nodes which the object to be processed may reach. In this way, the first end may determine, based on the first feature information, a leaf node that the object to be processed may reach and a target leaf node that the object to be processed may not reach from the tree model.
In the present disclosure, it is not always possible to determine leaf nodes (specifically, determined by the structure of the tree model and nodes managed by the first end and the second end, respectively) that can be reached by the object to be processed based on the feature information (the first feature information or the second feature information), and if the leaf nodes that can be reached by the object to be processed are determined based on the feature information, the leaf nodes that can be reached by the object to be processed are also referred to as leaf nodes that can be reached by the object to be processed for convenience of description.
Continuing with the example of fig. 3, the node 301 in the tree model of fig. 3 is a node managed by the first end, and the first end may determine, based on the first feature information, a splitting direction of the object to be processed at the node 301, for example, splitting to the right, and then the object to be processed may reach the node 303 after the first splitting, and the node 302 is a node that the object to be processed does not reach, and then the two leaf nodes 304 and 305 connected by the node 302 are leaf nodes that the object to be processed does not reach, that is, target leaf nodes. However, since the node 303 is a node managed by the second end, the first end cannot know the splitting direction of the object to be processed at the node 303, and from the perspective of the first end, the object to be processed may be split leftward or rightward at the node 303, and therefore, the first end may use two leaf nodes 306 and 306 connected to the node 303 as leaf nodes that the object to be processed may reach.
In step 203, the first end performs the processing step for the leaf nodes included in the tree model.
In this embodiment, for each of the leaf nodes included in the tree model, the first end may perform the following processing steps (step 2031-step 2032):
step 2031, in response to the leaf node being the target leaf node, encrypt 0 to obtain the encryption weight corresponding to the target leaf node.
Step 2032, in response to that the leaf node is not the target leaf node, encrypting the weight of the leaf node to obtain the encryption weight corresponding to the leaf node.
Specifically, the first end may encrypt the weight of 0 or the leaf node in a homomorphic encryption manner, so that an encryption manner corresponding to an encryption score obtained by summing the encryption weights subsequently may be the same as an encryption manner corresponding to the encryption weights, and further, after obtaining the encryption score, the first end may decrypt the encryption score based on the encryption manner in which the weight of 0 or the leaf node is encrypted, so as to obtain the score of the object to be processed.
In practice, in order to determine the leaf node that the object to be processed actually reaches in the tree model, the second end needs to know the leaf node that the object to be processed may reach at the first end, and then the leaf node that the object to be processed may reach at the first end and the leaf node that the object to be processed may reach at the second end take an intersection, so that the leaf node that the object to be processed actually reaches in the tree model can be obtained. However, if the first end directly discloses the leaf node that the object to be processed may reach at the first end to the second end, the second end will know the target leaf node that the object to be processed may not reach at the first end, which will cause the second end to know the splitting direction of the object to be processed at the node managed by the first end, thereby causing the privacy of the first end to be revealed. For example, continuing with the example of fig. 3, if the first end discloses to the second end that the leaf node 306 and the leaf node 307 may be reached, the second end may know that the object to be processed does not reach the leaf node 305 and the leaf node 306, and may further know that the splitting direction of the object to be processed at the node 301 managed by the first end is right splitting.
In the scheme of this embodiment, the first end may disclose the weight of the leaf node to the second end in an encrypted manner, which will make the second end unable to distinguish the leaf node that the object to be processed may reach at the first end from the target leaf node that may not reach according to the encrypted weight. And for a target leaf node which cannot be reached by the object to be processed at the first end, 0 can be used as a virtual weight of the target leaf node, the encryption weight obtained by encrypting 0 is retransmitted to the second end, and when the score of the object to be processed is obtained through summation, the target leaf node can not influence the summation result, so that the accuracy of the obtained score of the object to be processed can be ensured.
Continuing with the example of fig. 3, for the four leaf nodes in the tree model shown in fig. 3, the leaf nodes 304 and 305 are target leaf nodes that the object to be processed does not reach, and the first end may use 0 as the virtual weight of the two target leaf nodes, encrypt 0, and obtain the encryption weight x corresponding to the target leaf nodes 304 and 305; the leaf nodes 306 and 307 are leaf nodes that the object to be processed may reach, the first end may obtain a weight "0.4" of the leaf node 306 and a weight "0.2" of the leaf node 307, then encrypt the weight "0.4" to obtain an encryption weight y corresponding to the leaf node 306, and encrypt the weight "0.2" to obtain an encryption weight z corresponding to the leaf node 307.
In step 204, the first peer sends the obtained encryption weights and a processing request for the object to be processed to the second peer.
In this embodiment, based on the encryption weight obtained in step 203, the first side may retransmit the encryption weight to the second side (e.g., the second side shown in fig. 1), and transmit a processing request for the above-described object to be processed to the second side. The processing request may be used to request processing of the object to be processed. Specifically, the processing request may include an identifier (for example, an ID) of the object to be processed, and after receiving the processing request, the second end may process the object to be processed based on the identifier of the object to be processed.
Continuing with the above example, the first end may send the above encryption weights x, y, z to the second end. Furthermore, the second end can only obtain the encrypted information, and cannot directly or indirectly obtain the splitting direction of the object to be processed at the first end.
In step 205, the second end obtains second characteristic information of the object to be processed in response to receiving the encryption weight and the processing request for the object to be processed, which are sent by the first end.
In this embodiment, the second end may obtain the second feature information of the object to be processed from a remote location or a local location through a wired connection manner or a wireless connection manner in response to receiving the encryption weight and the processing request sent by the first end. And the second characteristic information of the object to be processed can be used for characterizing the characteristic of the object to be processed. Specifically, the features represented by the second feature information acquired by the second end may be the same as the features corresponding to the nodes managed by the second end.
As an example, the feature corresponding to the node managed by the second end is a height feature, and then the second feature information may be information for characterizing the height feature of the object to be processed (e.g. the user).
In step 206, the second end screens out at least two leaf nodes that the object to be processed may reach from the leaf nodes included in the tree model as at least two candidate leaf nodes based on the second feature information.
In this embodiment, based on the second feature information obtained in step 205, the second end may screen out at least two leaf nodes that the object to be processed may reach from the leaf nodes included in the tree model as at least two candidate leaf nodes.
Specifically, for a node managed by the second end in the tree model, the second end may determine, based on the second feature information, a splitting direction of the object to be processed at the node, and then determine, according to the splitting direction, a leaf node that the object to be processed does not reach after the node is split. In particular, the second end may further determine a leaf node that the object to be processed can reach according to the splitting direction. For a node managed by the first end in the tree model, the second end cannot know the splitting direction of the object to be processed in the node, and in this case, the second end can use each splitting direction of the object to be processed in the node as a possible splitting direction, and further use leaf nodes corresponding to the node as leaf nodes which the object to be processed may reach. Thus, the second end can determine at least two candidate leaf nodes which are possibly reached by the object to be processed from the tree model based on the second characteristic information.
It can be understood that, here, since the leaf nodes corresponding to the nodes managed by the first end are all taken as candidate leaf nodes, and the leaf nodes under the nodes managed by the first end necessarily include the leaf nodes that the object to be processed may reach at the first end and the target leaf nodes that the object to be processed may not reach, the candidate leaf nodes include the leaf nodes that the object to be processed may reach at the first end and the target leaf nodes that the object to be processed may not reach.
Continuing with the above example, after the second end obtains the encryption weights x, y, z, the object to be processed may be split in the tree model based on the second characteristic information. Specifically, for the corresponding node 301, since the node 301 is a node managed by the first end, the second end cannot know the splitting direction of the object to be processed at the node 301, and from the perspective of the second end, the object to be processed may be split leftward or rightward at the node 301, and therefore, the second end may use both the node 302 and the node 303 as nodes to which the object to be processed may reach. Then, for the node 302, the second end may determine the splitting direction of the object to be processed at the node 302 based on the second characteristic information, for example, splitting to the right, and then the second end may determine the leaf node 305 as a candidate leaf node that the object to be processed may reach at the second end. For node 303, the second end may determine the splitting direction of the object to be processed at node 303 based on the second feature information, for example, splitting to the left, and then the second end may determine that leaf node 306 is a candidate leaf node that the object to be processed may reach at the second end. Further, the second end may obtain two candidate leaf nodes, which are candidate leaf node 305 and candidate leaf node 306, respectively, based on the second feature information. As can be seen from the above example, the candidate leaf node 305 is a target leaf node that the object to be processed does not reach at the first end.
Further, in the above example, the candidate leaf node 306 is an intersection of a leaf node that may be reached by the object to be processed at the first end and a leaf node that may be reached by the object to be processed at the second end, and it is known that the candidate leaf node 306 is a leaf node that is actually reached by the object to be processed in the tree model.
In step 207, the second end sums the encryption weights corresponding to the at least two candidate leaf nodes to obtain the encryption score of the object to be processed in the tree model.
In this embodiment, based on the at least two candidate leaf nodes screened in step 206, the second end may extract at least two encryption weights corresponding to the screened at least two candidate leaf nodes from the received encryption weights, and sum the extracted at least two encryption weights to obtain an encryption score of the object to be processed in the tree model. Wherein the encryption score may be a score of the encrypted object to be processed. The score of the object to be processed may be a numerical value for characterizing the possibility that the object to be processed belongs to the preset category. The higher the score, the greater the likelihood that the object to be processed may be characterized as belonging to a preset category. The preset categories may be categories that the tree model can predict.
Continuing with the above example, for the screened candidate leaf node 305 and candidate leaf node 306, the second end may obtain the encryption weight x corresponding to the candidate leaf node 305 and the encryption weight y corresponding to the candidate leaf node, and sum the two to obtain the encryption score "x + y".
In practice, the number of trees included in the tree model may be arbitrary, and in some alternative implementations of this embodiment, the tree model may include at least two trees; and the second end may perform the above step 206 and the above step 207 by:
first, for a tree of the at least two trees comprised by the tree model, the second end may perform the following steps: screening at least two leaf nodes which are possibly reached by an object to be processed from the leaf nodes included in the tree as at least two candidate leaf nodes; and summing the encryption weights corresponding to at least two candidate leaf nodes of the tree to obtain an initial encryption score corresponding to the tree.
Then, the second end may sum at least two initial encryption scores corresponding to at least two trees included in the tree model to obtain an encryption score of the object to be processed in the tree model.
The tree model in this implementation includes at least two trees, and then the second end needs to sum at least two initial encryption scores corresponding to the at least two trees when generating the encryption score, so the encryption score sent by the second end to the first end is obtained by counting the results of the trees, and when the first end cannot obtain the splitting result corresponding to the second end on the object to be processed, the splitting result corresponding to each tree in the tree model can be reduced, and then the possibility that the first end presumes the splitting direction of the object to be processed at the second end based on the splitting result of each tree can be reduced, which is helpful for protecting the privacy of the second end and improving the security of the data of the second end.
In step 208, the second end sends the encrypted results to the first end.
In this embodiment, after obtaining the encryption score, the second end may send the encryption score to the first end, so that the first end classifies the object to be processed based on the encryption score.
In some optional implementations of this embodiment, after the first end receives the encryption score, the following steps may be performed:
first, the received encrypted score is decrypted to obtain a target score.
Specifically, the first end may decrypt the encrypted score based on the way of encrypting 0 or the weight of the leaf node in step 203 to obtain the target score.
Continuing with the above example, the first peer may receive the encrypted score "x + y" sent by the second peer and decrypt "x + y" to obtain a target score of 0.4.
It can be understood that, since the encryption weight "x" corresponding to the leaf node 305 is the encryption performed on the virtual weight "0", a decryption result obtained when decrypting "x" is "0", and further the decryption result corresponding to "x" does not affect the target score obtained by summing, so that the leaf node 305 that the object to be processed does not reach at the first end can be removed from the leaf nodes used for generating the target score, and further the target score determined by the weight corresponding to the leaf node 306 that the object to be processed actually reaches in the tree model is obtained. Thereby, the effectiveness and accuracy of the obtained target score can be improved.
And secondly, classifying the objects to be processed based on the target scores.
Based on the target score obtained in the first step, the first end may classify the object to be processed.
Specifically, as an example, the first end may determine whether the target score is greater than or equal to a target threshold, and in response to the target score being greater than or equal to the target threshold, classify the object to be processed (e.g., the user) as a first class (e.g., an obese class); in response to the target score being less than the target threshold, the subject to be treated is classified into a second class (e.g., a non-obese class).
In the system provided by the above embodiment of the present disclosure, the first end may encrypt the virtual weight (that is, 0) of the target leaf node and the real weight of the leaf node except the target leaf node, respectively, and then send the encrypted virtual weight and the real weight to the second end, where the encryption technology may make the second end unable to distinguish the target leaf node, and thus unable to know the splitting direction of the first end, and the encryption of 0 may make the contribution degree of the target leaf node to the score of the object to be processed be 0, and thus may ensure the accuracy of the score of the object to be processed obtained by summing up, and therefore, the present disclosure may protect the privacy of the first end and improve the security of the data of the first end while ensuring that the first end can obtain the accurate score of the object to be processed; in addition, the second end in the disclosure only needs to distribute the encrypted result obtained by summing to the first end, and does not need to disclose the splitting direction to the first end, so the disclosure can also protect the privacy of the second end.
With continued reference to FIG. 4, a flow 400 of one embodiment of an information processing method according to the present disclosure is shown. The information processing method comprises the following steps:
In this embodiment, a first end (e.g., the first end shown in fig. 1) of an execution main body of the information processing method may acquire the first feature information of the object to be processed from a remote location or a local location by a wired connection manner or a wireless connection manner. Wherein the first end belongs to a predetermined information processing system (e.g., the information processing system corresponding to fig. 2). The information processing system may include a first end and a second end, and the first end and the second end may have the same tree model, which may be various models composed of trees. Specifically, the tree model may be a model obtained by the first end and the second end through joint learning training. The first and second ends may manage a portion of the nodes in the tree model, respectively. The first end may have weights of leaf nodes of the tree model. Here, the nodes of the tree model may be split, while the leaf nodes of the tree model may not be split.
In this embodiment, the object to be processed may be an object to be processed, and specifically, may be various objects that can be processed by the tree model, for example, an image, a text, a video, an audio, and the like. The first characteristic information of the object to be processed may be used to characterize the characteristic of the object to be processed. Specifically, the features represented by the first feature information acquired by the first end may be the same as the features corresponding to the nodes managed by the first end.
In this embodiment, based on the first feature information obtained in step 401, the first end may screen out leaf nodes that are not reached by the object to be processed from the leaf nodes included in the tree model as target leaf nodes.
In step 403, the processing step is performed for the leaf nodes included in the tree model.
In this embodiment, for a leaf node included in the tree model, the first end may perform the following processing steps (step 4031-step 4032):
In some optional implementations of this embodiment, the first end may perform step 4031 and step 4032 as described above in the following manner:
in response to that the leaf node is a target leaf node, encrypting 0 by using a homomorphic encryption mode to obtain an encryption weight corresponding to the target leaf node; and in response to that the leaf node is not the target leaf node, encrypting the weight of the leaf node by using a homomorphic encryption mode to obtain the encryption weight corresponding to the leaf node. Step 404, sending the obtained encryption weight and the processing request for the object to be processed to the second end.
In this embodiment, based on the encryption weight obtained in step 403, the first side may retransmit the encryption weight to the second side (e.g., the second side shown in fig. 1), and transmit a processing request for the above-described object to be processed to the second side. The processing request may be used to request processing of the object to be processed.
In this embodiment, after transmitting the encryption weight and the processing request to the second peer, the first peer may receive an encryption score generated based on the encryption weight from the second peer. Wherein the encryption score may be a score of the encrypted object to be processed. The score of the object to be processed may be a numerical value for characterizing the possibility that the object to be processed belongs to the preset category. The higher the score, the greater the likelihood that the object to be processed may be characterized as belonging to a preset category. The preset categories may be categories that the tree model can predict.
Specifically, the encryption score may be obtained by the second end through the following steps:
first, second characteristic information of the object to be processed is obtained.
And the second characteristic information of the object to be processed can be used for characterizing the characteristic of the object to be processed. Specifically, the features represented by the second feature information acquired by the second end may be the same as the features corresponding to the nodes managed by the second end.
And secondly, screening at least two leaf nodes which are possibly reached by the object to be processed from the leaf nodes included in the tree model based on the second characteristic information to serve as at least two candidate leaf nodes.
Wherein the at least two candidate leaf nodes include a target leaf node.
And thirdly, summing the encryption weights corresponding to the at least two candidate leaf nodes to obtain the encryption score of the object to be processed in the tree model.
In some optional implementation manners of this embodiment, after receiving the encryption score, the first end may further perform the following steps:
first, the received encrypted score is decrypted to obtain a target score.
And secondly, classifying the objects to be processed based on the target scores.
It should be noted that the first terminal and the second terminal in the embodiment corresponding to fig. 4 may have similar or identical features to the first terminal and the second terminal in the embodiment corresponding to fig. 2, respectively, and the description of the first terminal and the second terminal in the embodiment corresponding to fig. 2 also applies to the embodiment corresponding to fig. 4. To avoid repetition, further description is omitted here.
The method provided by the above embodiment of the present disclosure may be executed by a first end in an information processing system, where the first end may encrypt a virtual weight (that is, 0) of a target leaf node and a real weight of a leaf node other than the target leaf node, respectively, and then send the encrypted virtual weight and the real weight to a second end, and an encryption technique may enable the second end to be unable to distinguish the target leaf node, and further unable to know a splitting direction of the first end, and an encryption of 0 may enable a contribution degree of the target leaf node to a score of an object to be processed to be 0, and further may ensure accuracy of the score of the object to be processed obtained by summing up.
With further reference to FIG. 5, a flow 500 of yet another embodiment of an information processing method is shown. The process 500 of the information processing method includes the following steps:
In this embodiment, the second end of the execution subject of the information processing method may acquire the second feature information of the object to be processed in response to receiving the encryption weight and the processing request for the object to be processed, which are sent by the first end, through a wired connection manner or a wireless connection manner. Wherein the second end belongs to a predetermined information processing system (e.g., the information processing system corresponding to fig. 2). The information processing system may include a first end and a second end, and the first end and the second end may have the same tree model, which may be various models composed of trees. Specifically, the tree model may be a model obtained by the first end and the second end through joint learning training. The first and second ends may manage a portion of the nodes in the tree model, respectively. The first end may have weights of leaf nodes of the tree model. Here, the nodes of the tree model may be split, while the leaf nodes of the tree model may not be split.
In this embodiment, the obtained encryption weights may include encryption weights corresponding to each leaf node in the tree model. The second characteristic information of the object to be processed may be used to characterize the characteristic of the object to be processed. Specifically, the features represented by the second feature information acquired by the second end may be the same as the features corresponding to the nodes managed by the second end. The processing request may be for requesting processing of the object to be processed.
In this embodiment, the encryption weight may be obtained by the first end by:
first, first characteristic information of an object to be processed is acquired.
The first characteristic information of the object to be processed can be used for characterizing the characteristic of the object to be processed. Specifically, the features represented by the first feature information acquired by the first end may be the same as the features corresponding to the nodes managed by the first end.
And secondly, screening leaf nodes which cannot be reached by the object to be processed from the leaf nodes included in the tree model based on the first characteristic information to serve as target leaf nodes.
Thirdly, for the leaf nodes included in the tree model, executing the following processing steps: in response to the leaf node being the target leaf node, encrypting 0 to obtain an encryption weight corresponding to the target leaf node; and in response to the fact that the leaf node is not the target leaf node, encrypting the weight of the leaf node to obtain the encryption weight corresponding to the leaf node.
In this embodiment, based on the second feature information obtained in step 501, the second end may screen out at least two leaf nodes that the object to be processed may reach from the leaf nodes included in the tree model as at least two candidate leaf nodes. Wherein the at least two candidate leaf nodes may include a target leaf node screened by the first end.
In this embodiment, based on the at least two candidate leaf nodes screened in step 502, the second end may extract at least two encryption weights corresponding to the screened at least two candidate leaf nodes from the encryption weights obtained in step 501, and sum the extracted at least two encryption weights to obtain an encryption score of the object to be processed in the tree model. Wherein the encryption score may be a score of the encrypted object to be processed. The score of the object to be processed may be a numerical value for characterizing the possibility that the object to be processed belongs to the preset category. The higher the score, the greater the likelihood that the object to be processed may be characterized as belonging to a preset category. The preset categories may be categories that the tree model can predict.
In some alternative implementations of this embodiment, the tree model includes at least two trees; and the second end may perform the above steps 502 and 503 by:
for a tree of the at least two trees comprised by the tree model, performing the following steps: screening at least two leaf nodes which are possibly reached by the object to be processed from the leaf nodes included in the tree as at least two candidate leaf nodes; summing the encryption weights corresponding to at least two candidate leaf nodes of the tree to obtain an initial encryption score corresponding to the tree; and summing at least two initial encryption scores corresponding to at least two trees included in the tree model to obtain the encryption score of the object to be processed in the tree model.
In this embodiment, based on the encryption score obtained in step 503, the second end may send the encryption score to the first end, so that the first end classifies the object to be processed based on the encryption score.
It should be noted that the first terminal and the second terminal in the embodiment corresponding to fig. 5 may have similar or identical features to the first terminal and the second terminal in the embodiment corresponding to fig. 2, respectively, and the description of the first terminal and the second terminal in the embodiment corresponding to fig. 2 also applies to the embodiment corresponding to fig. 5. To avoid repetition, further description is omitted here.
The method provided by the above embodiment of the present disclosure may be executed by a second end in the information processing system, and the second end may complete score prediction for the object to be processed based on the encryption weight provided by the first end, and in the whole process, the second end only needs to distribute the encryption result obtained by summing to the first end, and does not need to disclose the splitting direction of the object to be processed on the managed node to the first end, which is beneficial to protecting the privacy of the second end and improving the security of the data of the second end.
With further reference to fig. 6, as an implementation of the method shown in the above figures, the present disclosure provides an embodiment of an information processing apparatus, which corresponds to the embodiment of the method shown in fig. 4, the apparatus may be configured at a first end in an information processing system, the information processing system may include a first end and a second end, the first end and the second end have the same tree model, the first end and the second end respectively manage part of nodes in the tree model, and the first end has weights of leaf nodes of the tree model.
As shown in fig. 6, the information processing apparatus 600 of the present embodiment includes: a first acquisition unit 601 configured to acquire first feature information of an object to be processed; a first screening unit 602 configured to screen, as a target leaf node, a leaf node that is not reached by the object to be processed from leaf nodes included in the tree model based on the first feature information; an execution unit 603 configured to, for a leaf node included in the tree model, perform the following processing steps: in response to the leaf node being the target leaf node, encrypting 0 to obtain an encryption weight corresponding to the target leaf node; in response to that the leaf node is not the target leaf node, encrypting the weight of the leaf node to obtain an encryption weight corresponding to the leaf node; a first transmitting unit 604 configured to transmit the obtained encryption weight and a processing request for the object to be processed to the second end; and a receiving unit 605 configured to receive the encryption score from the second end, wherein the target encryption score is obtained by the second end by: acquiring second characteristic information of the object to be processed; screening at least two leaf nodes which are possibly reached by the object to be processed from the leaf nodes included in the tree model based on the second characteristic information to serve as at least two candidate leaf nodes, wherein the at least two candidate leaf nodes include a target leaf node; and summing the encryption weights corresponding to the at least two candidate leaf nodes to obtain the encryption score of the object to be processed in the tree model.
In this embodiment, the first acquisition unit 601 of the information processing apparatus 600 may acquire the first feature information of the object to be processed from a remote or local place by a wired connection manner or a wireless connection manner. The object to be processed may be an object to be processed, and specifically, may be various objects that can be processed by the tree model. The first characteristic information of the object to be processed may be used to characterize the characteristic of the object to be processed. Specifically, the features represented by the first feature information acquired by the first end may be the same as the features corresponding to the nodes managed by the first end.
In this embodiment, based on the first feature information obtained by the first obtaining unit 601, the first screening unit 602 may screen out leaf nodes that are not reached by the object to be processed from the leaf nodes included in the tree model as target leaf nodes.
In this embodiment, for the leaf nodes included in the tree model, the execution unit 603 may perform the following processing steps (step 6031-step 6032):
step 6031, in response to that the leaf node is the target leaf node, encrypt 0 to obtain the encryption weight corresponding to the target leaf node.
In step 6032, in response to that the leaf node is not the target leaf node, the weight of the leaf node is encrypted to obtain an encryption weight corresponding to the leaf node.
In this embodiment, based on the encryption weight obtained by the execution unit 603, the first transmission unit 604 may retransmit the encryption weight to the second side and transmit a processing request for the above-described object to be processed to the second side. The processing request may be used to request processing of the object to be processed.
In this embodiment, after transmitting the encryption weight and the processing request to the second end, the receiving unit 605 of the apparatus 600 may receive the encryption score generated based on the encryption weight from the second end. Wherein the encryption score may be a score of the encrypted object to be processed. The score of the object to be processed may be a numerical value for characterizing the possibility that the object to be processed belongs to the preset category. The higher the score, the greater the likelihood that the object to be processed may be characterized as belonging to a preset category. The preset categories may be categories that the tree model can predict.
Specifically, the encryption score may be obtained by the second end through the following steps: acquiring second characteristic information of the object to be processed; screening at least two leaf nodes which are possibly reached by the object to be processed from the leaf nodes included in the tree model based on the second characteristic information to serve as at least two candidate leaf nodes; and summing the encryption weights corresponding to the at least two candidate leaf nodes to obtain the encryption score of the object to be processed in the tree model.
In some optional implementations of this embodiment, the apparatus 600 may further include: a decryption unit (not shown in the figure) configured to decrypt the received encrypted score to obtain a target score; a classification unit (not shown in the figure) configured to classify the object to be processed based on the target score.
In some optional implementations of this embodiment, the execution unit 603 is further configured to: in response to that the leaf node is a target leaf node, encrypting 0 by using a homomorphic encryption mode to obtain an encryption weight corresponding to the target leaf node; and in response to that the leaf node is not the target leaf node, encrypting the weight of the leaf node by using a homomorphic encryption mode to obtain the encryption weight corresponding to the leaf node. It is understood that the elements described in the apparatus 600 correspond to various steps in the method described in fig. 4. Thus, the operations, features and resulting advantages described above with respect to the method are also applicable to the apparatus 600 and the units included therein, and are not described herein again.
The device 600 provided by the above embodiment of the present disclosure may be configured on the first end, and then the first end configured with the device 600 may encrypt the virtual weight (that is, 0) of the target leaf node and the real weight of the leaf node except the target leaf node respectively and send the encrypted virtual weight and the real weight to the second end, where the encryption technique may cause the second end to be unable to distinguish the target leaf node, and further unable to know the splitting direction of the first end, and the encryption of 0 may cause the contribution degree of the target leaf node to the score of the object to be processed to be 0, and further may ensure the accuracy of the score of the object to be processed obtained by summing, and therefore, the present disclosure may protect the privacy of the first end and improve the security of the data of the first end under the condition that the first end is ensured to obtain the accurate score of the object to be processed.
With further reference to fig. 7, as an implementation of the method shown in the above figures, the present disclosure provides another embodiment of an information processing apparatus, the apparatus embodiment corresponds to the method embodiment shown in fig. 5, the apparatus may be configured at a second end in an information processing system, the information processing system may include a first end and a second end, the first end and the second end have the same tree model, the first end and the second end respectively manage part of nodes in the tree model, and the first end has weights of leaf nodes of the tree model.
As shown in fig. 7, the information processing apparatus 700 of the present embodiment includes: a second obtaining unit 701 configured to obtain second feature information of the object to be processed in response to receiving the encryption weight and the processing request for the object to be processed, the encryption weight being obtained by the first end by: acquiring first characteristic information of an object to be processed; based on the first characteristic information, leaf nodes which cannot be reached by the object to be processed are screened out from the leaf nodes included in the tree model and serve as target leaf nodes; for leaf nodes included in the tree model, the following processing steps are performed: in response to the leaf node being the target leaf node, encrypting 0 to obtain an encryption weight corresponding to the target leaf node; in response to that the leaf node is not the target leaf node, encrypting the weight of the leaf node to obtain an encryption weight corresponding to the leaf node; a second screening unit 702 configured to screen, based on the second feature information, at least two leaf nodes that are possibly reached by the object to be processed from the leaf nodes included in the tree model as at least two candidate leaf nodes, where the at least two candidate leaf nodes include a target leaf node; a summing unit 703 configured to sum the encryption weights corresponding to the at least two candidate leaf nodes to obtain an encryption score of the object to be processed in the tree model; a second sending unit 704 configured to send the encrypted result to the first end.
In this embodiment, the second acquisition unit 701 of the information processing apparatus 700 may acquire the second feature information of the object to be processed in response to receiving the encryption weight and the processing request for the object to be processed, which are transmitted by the first end, through a wired connection manner or a wireless connection manner. The obtained encryption weight may include an encryption weight corresponding to each leaf node in the tree model. The second characteristic information of the object to be processed may be used to characterize the characteristic of the object to be processed. Specifically, the features represented by the second feature information acquired by the second end may be the same as the features corresponding to the nodes managed by the second end. The processing request may be for requesting processing of the object to be processed.
In this embodiment, the encryption weight may be obtained by the first end by: acquiring first characteristic information of an object to be processed; based on the first characteristic information, leaf nodes which cannot be reached by the object to be processed are screened out from the leaf nodes included in the tree model and serve as target leaf nodes; for leaf nodes included in the tree model, the following processing steps are performed: in response to the leaf node being the target leaf node, encrypting 0 to obtain an encryption weight corresponding to the target leaf node; and in response to the fact that the leaf node is not the target leaf node, encrypting the weight of the leaf node to obtain the encryption weight corresponding to the leaf node.
In this embodiment, based on the second feature information obtained by the second obtaining unit 701, the second screening unit 702 may screen out at least two leaf nodes that may be reached by the object to be processed from the leaf nodes included in the tree model as at least two candidate leaf nodes. Wherein the at least two candidate leaf nodes may include a target leaf node screened by the first end.
In this embodiment, based on the at least two candidate leaf nodes screened by the second screening unit 702, the summing unit 703 may extract at least two encryption weights corresponding to the screened at least two candidate leaf nodes from the encryption weights obtained by the second obtaining unit 701, and sum the extracted at least two encryption weights to obtain an encryption score of the object to be processed in the tree model. Wherein the encryption score may be a score of the encrypted object to be processed. The score of the object to be processed may be a numerical value for characterizing the possibility that the object to be processed belongs to the preset category. The higher the score, the greater the likelihood that the object to be processed may be characterized as belonging to a preset category. The preset categories may be categories that the tree model can predict.
In this embodiment, based on the encryption score obtained by the summing unit 703, the second transmitting unit 704 may transmit the encryption score to the first end so that the first end classifies the object to be processed based on the encryption score.
It is understood that the elements described in the apparatus 700 correspond to various steps in the method described in fig. 5. Thus, the operations, features and resulting advantages described above with respect to the method are also applicable to the apparatus 700 and the units included therein, and will not be described herein again.
In some alternative implementations of this embodiment, the tree model includes at least two trees; and the second screening unit 702 and the summing unit 703 are further configured to: for a tree of the at least two trees comprised by the tree model, performing the following steps: screening at least two leaf nodes which are possibly reached by an object to be processed from the leaf nodes included in the tree as at least two candidate leaf nodes; summing the encryption weights corresponding to at least two candidate leaf nodes of the tree to obtain an initial encryption score corresponding to the tree; and summing at least two initial encryption scores corresponding to at least two trees included in the tree model to obtain the encryption score of the object to be processed in the tree model. The device 700 provided by the above embodiment of the present disclosure may be configured on the second end, and then the second end configured with the device 700 may complete score prediction for the object to be processed based on the encryption weight provided by the first end, and in the whole process, the second end only needs to distribute the encryption score obtained by summing to the first end, and does not need to disclose the splitting direction of the object to be processed on the managed node to the first end, which is beneficial to protecting the privacy of the second end and improving the security of the data of the second end.
Referring now to FIG. 8, a block diagram of an electronic device (e.g., the server of FIG. 1) 800 suitable for use in implementing embodiments of the present disclosure is shown. The terminal device in the embodiments of the present disclosure may include, but is not limited to, a mobile terminal such as a mobile phone, a notebook computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet computer), a PMP (portable multimedia player), a vehicle terminal (e.g., a car navigation terminal), and the like, and a stationary terminal such as a digital TV, a desktop computer, and the like. The electronic device shown in fig. 8 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present disclosure.
As shown in fig. 8, an electronic device 800 may include a processing means (e.g., central processing unit, graphics processor, etc.) 801 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM)802 or a program loaded from a storage means 808 into a Random Access Memory (RAM) 803. In the RAM803, various programs and data necessary for the operation of the electronic apparatus 800 are also stored. The processing apparatus 801, the ROM 802, and the RAM803 are connected to each other by a bus 804. An input/output (I/O) interface 805 is also connected to bus 804.
Generally, the following devices may be connected to the I/O interface 805: input devices 806 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, etc.; output devices 807 including, for example, a Liquid Crystal Display (LCD), speakers, vibrators, and the like; storage 808 including, for example, magnetic tape, hard disk, etc.; and a communication device 809. The communication means 809 may allow the electronic device 800 to communicate wirelessly or by wire with other devices to exchange data. While fig. 8 illustrates an electronic device 800 having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided.
In particular, according to an embodiment of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication means 809, or installed from the storage means 808, or installed from the ROM 802. The computer program, when executed by the processing apparatus 801, performs the above-described functions defined in the methods of the embodiments of the present disclosure.
It should be noted that the computer readable medium in the present disclosure may be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device. The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to: acquiring first characteristic information of an object to be processed; based on the first characteristic information, leaf nodes which cannot be reached by the object to be processed are screened out from the leaf nodes included in the tree model and serve as target leaf nodes; for leaf nodes included in the tree model, the following processing steps are performed: in response to the leaf node being the target leaf node, encrypting 0 to obtain an encryption weight corresponding to the target leaf node; in response to that the leaf node is not the target leaf node, encrypting the weight of the leaf node to obtain an encryption weight corresponding to the leaf node; sending the obtained encryption weight and a processing request aiming at the object to be processed to a second end; and receiving the encryption score from the second end, wherein the target encryption score is obtained by the second end by: acquiring second characteristic information of the object to be processed; screening at least two leaf nodes which are possibly reached by the object to be processed from the leaf nodes included in the tree model based on the second characteristic information to serve as at least two candidate leaf nodes, wherein the at least two candidate leaf nodes include a target leaf node; and summing the encryption weights corresponding to the at least two candidate leaf nodes to obtain the encryption score of the object to be processed in the tree model.
Alternatively, the electronic device may be further caused to: in response to receiving the encryption weight sent by the first end and the processing request aiming at the object to be processed, acquiring second characteristic information of the object to be processed, wherein the encryption weight is obtained by the first end through the following steps: acquiring first characteristic information of an object to be processed; based on the first characteristic information, leaf nodes which cannot be reached by the object to be processed are screened out from the leaf nodes included in the tree model and serve as target leaf nodes; for leaf nodes included in the tree model, the following processing steps are performed: in response to the leaf node being the target leaf node, encrypting 0 to obtain an encryption weight corresponding to the target leaf node; in response to that the leaf node is not the target leaf node, encrypting the weight of the leaf node to obtain an encryption weight corresponding to the leaf node; screening at least two leaf nodes which are possibly reached by the object to be processed from the leaf nodes included in the tree model based on the second characteristic information to serve as at least two candidate leaf nodes, wherein the at least two candidate leaf nodes include a target leaf node; summing the encryption weights corresponding to the at least two candidate leaf nodes to obtain the encryption score of the object to be processed in the tree model; the encrypted results are sent to the first end.
Computer program code for carrying out operations for aspects of the present disclosure may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present disclosure may be implemented by software or hardware. The described units may also be provided in a processor, and may be described as: a processor includes a first obtaining unit, a first screening unit, an execution unit, a first sending unit, and a receiving unit. Where the names of these units do not in some cases constitute a limitation on the unit itself, for example, a receiving unit may also be described as a "unit that receives an encryption score".
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the disclosure herein is not limited to the particular combination of features described above, but also encompasses other embodiments in which any combination of the features described above or their equivalents does not depart from the spirit of the disclosure. For example, the above features and (but not limited to) the features disclosed in this disclosure having similar functions are replaced with each other to form the technical solution.
Claims (15)
1. An information processing system, the system comprising a first side and a second side, the first side and the second side having a same tree model, the first side and the second side managing part of nodes in the tree model, respectively, the first side having weights of leaf nodes of the tree model, wherein,
the first end is configured to: acquiring first characteristic information of an object to be processed; based on the first characteristic information, leaf nodes which are not reached by the object to be processed are screened out from the leaf nodes included in the tree model and serve as target leaf nodes; for leaf nodes included in the tree model, performing the following processing steps: in response to the leaf node being the target leaf node, encrypting 0 to obtain an encryption weight corresponding to the target leaf node; in response to that the leaf node is not the target leaf node, encrypting the weight of the leaf node to obtain an encryption weight corresponding to the leaf node; sending the obtained encryption weight and a processing request for the object to be processed to the second end;
the second end is configured to: acquiring second characteristic information of the object to be processed in response to the received encryption weight sent by the first end and a processing request aiming at the object to be processed; screening at least two leaf nodes which are possibly reached by the object to be processed from the leaf nodes included in the tree model based on the second characteristic information to serve as at least two candidate leaf nodes, wherein the at least two candidate leaf nodes include a target leaf node; summing the encryption weights corresponding to the at least two candidate leaf nodes to obtain the encryption score of the object to be processed in the tree model; and sending the encrypted result to the first end.
2. The system of claim 1, wherein the tree model comprises at least two trees; and
the second end is further configured to:
for a tree of the at least two trees comprised by the tree model, performing the following steps: screening at least two leaf nodes which are possibly reached by the object to be processed from the leaf nodes included in the tree as at least two candidate leaf nodes; summing the encryption weights corresponding to at least two candidate leaf nodes of the tree to obtain an initial encryption score corresponding to the tree;
and summing at least two initial encryption scores corresponding to at least two trees included in the tree model to obtain the encryption score of the object to be processed in the tree model.
3. The system of claim 1 or 2, wherein the first end is further configured to:
decrypting the received encrypted score to obtain a target score;
and classifying the object to be processed based on the target score.
4. The system of claim 1 or 2, wherein the first end is further configured to:
in response to that the leaf node is a target leaf node, encrypting 0 by using a homomorphic encryption mode to obtain an encryption weight corresponding to the target leaf node;
and in response to that the leaf node is not the target leaf node, encrypting the weight of the leaf node by using a homomorphic encryption mode to obtain the encryption weight corresponding to the leaf node.
5. An information processing method performed by a first peer in an information processing system, the information processing system including the first peer and a second peer, the first peer and the second peer having a same tree model, the first peer and the second peer managing respective partial nodes in the tree model, the first peer having weights of leaf nodes of the tree model, the method comprising:
acquiring first characteristic information of an object to be processed;
based on the first characteristic information, leaf nodes which are not reached by the object to be processed are screened out from the leaf nodes included in the tree model and serve as target leaf nodes;
for leaf nodes included in the tree model, performing the following processing steps:
in response to the leaf node being the target leaf node, encrypting 0 to obtain an encryption weight corresponding to the target leaf node;
in response to that the leaf node is not the target leaf node, encrypting the weight of the leaf node to obtain an encryption weight corresponding to the leaf node;
sending the obtained encryption weight and a processing request for the object to be processed to the second end; and
receiving an encryption score from the second end, wherein the target encryption score is obtained by the second end by: acquiring second characteristic information of the object to be processed; screening at least two leaf nodes which are possibly reached by the object to be processed from the leaf nodes included in the tree model based on the second characteristic information to serve as at least two candidate leaf nodes, wherein the at least two candidate leaf nodes include a target leaf node; and summing the encryption weights corresponding to the at least two candidate leaf nodes to obtain the encryption score of the object to be processed in the tree model.
6. The method of claim 5, wherein the method further comprises:
decrypting the received encrypted score to obtain a target score;
and classifying the object to be processed based on the target score.
7. The method according to claim 5 or 6, wherein the encrypting 0 in response to the leaf node being a target leaf node, and obtaining the encryption weight corresponding to the target leaf node comprises:
in response to that the leaf node is a target leaf node, encrypting 0 by using a homomorphic encryption mode to obtain an encryption weight corresponding to the target leaf node; and
in response to that the leaf node is not the target leaf node, encrypting the weight of the leaf node, and obtaining the encryption weight corresponding to the leaf node includes:
and in response to that the leaf node is not the target leaf node, encrypting the weight of the leaf node by using a homomorphic encryption mode to obtain the encryption weight corresponding to the leaf node.
8. An information processing method performed by a second peer in an information processing system, the information processing system including a first peer and a second peer, the first peer and the second peer having a same tree model, the first peer and the second peer managing respective partial nodes in the tree model, the first peer having weights of leaf nodes of the tree model, the method comprising:
in response to receiving an encryption weight sent by a first end and a processing request aiming at the object to be processed, acquiring second characteristic information of the object to be processed, wherein the encryption weight is obtained by the first end through the following steps: acquiring first characteristic information of an object to be processed; based on the first characteristic information, leaf nodes which are not reached by the object to be processed are screened out from the leaf nodes included in the tree model and serve as target leaf nodes; for leaf nodes included in the tree model, performing the following processing steps: in response to the leaf node being the target leaf node, encrypting 0 to obtain an encryption weight corresponding to the target leaf node; in response to that the leaf node is not the target leaf node, encrypting the weight of the leaf node to obtain an encryption weight corresponding to the leaf node;
screening at least two leaf nodes which are possibly reached by the object to be processed from the leaf nodes included in the tree model based on the second characteristic information to serve as at least two candidate leaf nodes, wherein the at least two candidate leaf nodes include a target leaf node;
summing the encryption weights corresponding to the at least two candidate leaf nodes to obtain the encryption score of the object to be processed in the tree model;
and sending the encrypted result to the first end.
9. The method of claim 8, wherein the tree model comprises at least two trees; and
screening at least two leaf nodes which are possibly reached by the object to be processed from the leaf nodes included in the tree model based on the second characteristic information to serve as at least two candidate leaf nodes; summing the encryption weights corresponding to the at least two candidate leaf nodes, and obtaining the encryption score of the object to be processed in the tree model comprises:
for a tree of the at least two trees comprised by the tree model, performing the following steps: screening at least two leaf nodes which are possibly reached by the object to be processed from the leaf nodes included in the tree as at least two candidate leaf nodes; summing the encryption weights corresponding to at least two candidate leaf nodes of the tree to obtain an initial encryption score corresponding to the tree;
and summing at least two initial encryption scores corresponding to at least two trees included in the tree model to obtain the encryption score of the object to be processed in the tree model.
10. An information processing apparatus configured at a first end in an information processing system, the information processing system including the first end and a second end, the first end and the second end having a same tree model, the first end and the second end managing partial nodes in the tree model, respectively, the first end having weights of leaf nodes of the tree model, the apparatus comprising:
a first acquisition unit configured to acquire first feature information of an object to be processed;
a first screening unit configured to screen leaf nodes, which are not reached by the object to be processed, from the leaf nodes included in the tree model as target leaf nodes based on the first feature information;
an execution unit configured to execute the following processing steps for leaf nodes included in the tree model:
in response to the leaf node being the target leaf node, encrypting 0 to obtain an encryption weight corresponding to the target leaf node;
in response to that the leaf node is not the target leaf node, encrypting the weight of the leaf node to obtain an encryption weight corresponding to the leaf node;
a first transmitting unit configured to transmit the obtained encryption weight and a processing request for the object to be processed to the second end; and
a receiving unit configured to receive an encryption score from the second end, wherein the target encryption score is obtained by the second end by: acquiring second characteristic information of the object to be processed; screening at least two leaf nodes which are possibly reached by the object to be processed from the leaf nodes included in the tree model based on the second characteristic information to serve as at least two candidate leaf nodes, wherein the at least two candidate leaf nodes include a target leaf node; and summing the encryption weights corresponding to the at least two candidate leaf nodes to obtain the encryption score of the object to be processed in the tree model.
11. The apparatus of claim 10, wherein the apparatus further comprises:
a decryption unit configured to decrypt the received encrypted score to obtain a target score;
a classification unit configured to classify the object to be processed based on the target score.
12. The apparatus of claim 10 or 11, wherein the execution unit is further configured to:
in response to that the leaf node is a target leaf node, encrypting 0 by using a homomorphic encryption mode to obtain an encryption weight corresponding to the target leaf node;
and in response to that the leaf node is not the target leaf node, encrypting the weight of the leaf node by using a homomorphic encryption mode to obtain the encryption weight corresponding to the leaf node.
13. An information processing apparatus configured at a second end in an information processing system, the information processing system including a first end and a second end, the first end and the second end having a same tree model, the first end and the second end managing partial nodes in the tree model, respectively, the first end having weights of leaf nodes of the tree model, the apparatus comprising:
a second obtaining unit configured to obtain second feature information of the object to be processed in response to receiving the encryption weight and the processing request for the object to be processed, the encryption weight being obtained by the first end by: acquiring first characteristic information of an object to be processed; based on the first characteristic information, leaf nodes which are not reached by the object to be processed are screened out from the leaf nodes included in the tree model and serve as target leaf nodes; for leaf nodes included in the tree model, performing the following processing steps: in response to the leaf node being the target leaf node, encrypting 0 to obtain an encryption weight corresponding to the target leaf node; in response to that the leaf node is not the target leaf node, encrypting the weight of the leaf node to obtain an encryption weight corresponding to the leaf node;
a second screening unit configured to screen, from the leaf nodes included in the tree model, at least two leaf nodes that the object to be processed may reach as at least two candidate leaf nodes based on the second feature information, where the at least two candidate leaf nodes include a target leaf node;
the summing unit is configured to sum the encryption weights corresponding to the at least two candidate leaf nodes to obtain an encryption score of the object to be processed in the tree model;
a second transmitting unit configured to transmit the encrypted result to the first end.
14. An electronic device, comprising:
one or more processors;
a storage device having one or more programs stored thereon,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 5-9.
15. A computer-readable medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 5-9.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011018697.7A CN113807530B (en) | 2020-09-24 | 2020-09-24 | Information processing system, method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011018697.7A CN113807530B (en) | 2020-09-24 | 2020-09-24 | Information processing system, method and device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113807530A true CN113807530A (en) | 2021-12-17 |
| CN113807530B CN113807530B (en) | 2024-02-06 |
Family
ID=78943495
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011018697.7A Active CN113807530B (en) | 2020-09-24 | 2020-09-24 | Information processing system, method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113807530B (en) |
Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2007189597A (en) * | 2006-01-16 | 2007-07-26 | Univ Of Electro-Communications | Encryption device, encryption method, decoding device, and decoding method |
| CN102843372A (en) * | 2012-08-28 | 2012-12-26 | 西安交通大学 | Order-preserving encryption method based on random interval partition |
| WO2018122238A1 (en) * | 2016-12-30 | 2018-07-05 | Robert Bosch Gmbh | Method and system for fuzzy keyword search over encrypted data |
| CN110138802A (en) * | 2019-06-02 | 2019-08-16 | 四川虹微技术有限公司 | User's characteristic information acquisition methods, device, block chain node and storage medium |
| CN110457912A (en) * | 2019-07-01 | 2019-11-15 | 阿里巴巴集团控股有限公司 | Data processing method, device and electronic equipment |
| CN110874479A (en) * | 2018-08-29 | 2020-03-10 | 阿里巴巴集团控股有限公司 | Method and system for safely processing decision tree model, data terminal and processing terminal |
| CN110944011A (en) * | 2019-12-16 | 2020-03-31 | 支付宝(杭州)信息技术有限公司 | Joint prediction method and system based on tree model |
| CN111046408A (en) * | 2019-12-13 | 2020-04-21 | 支付宝(杭州)信息技术有限公司 | Judgment result processing method, query method, device, electronic equipment and system |
| US20200175426A1 (en) * | 2019-07-01 | 2020-06-04 | Alibaba Group Holding Limited | Data-based prediction results using decision forests |
| US20200204341A1 (en) * | 2017-01-20 | 2020-06-25 | Enveil, Inc. | Secure Machine Learning Analytics Using Homomorphic Encryption |
| CN111695697A (en) * | 2020-06-12 | 2020-09-22 | 深圳前海微众银行股份有限公司 | Multi-party combined decision tree construction method and device and readable storage medium |
-
2020
- 2020-09-24 CN CN202011018697.7A patent/CN113807530B/en active Active
Patent Citations (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2007189597A (en) * | 2006-01-16 | 2007-07-26 | Univ Of Electro-Communications | Encryption device, encryption method, decoding device, and decoding method |
| CN102843372A (en) * | 2012-08-28 | 2012-12-26 | 西安交通大学 | Order-preserving encryption method based on random interval partition |
| WO2018122238A1 (en) * | 2016-12-30 | 2018-07-05 | Robert Bosch Gmbh | Method and system for fuzzy keyword search over encrypted data |
| US20200204341A1 (en) * | 2017-01-20 | 2020-06-25 | Enveil, Inc. | Secure Machine Learning Analytics Using Homomorphic Encryption |
| CN110874479A (en) * | 2018-08-29 | 2020-03-10 | 阿里巴巴集团控股有限公司 | Method and system for safely processing decision tree model, data terminal and processing terminal |
| CN110138802A (en) * | 2019-06-02 | 2019-08-16 | 四川虹微技术有限公司 | User's characteristic information acquisition methods, device, block chain node and storage medium |
| CN110457912A (en) * | 2019-07-01 | 2019-11-15 | 阿里巴巴集团控股有限公司 | Data processing method, device and electronic equipment |
| US20200175426A1 (en) * | 2019-07-01 | 2020-06-04 | Alibaba Group Holding Limited | Data-based prediction results using decision forests |
| CN111046408A (en) * | 2019-12-13 | 2020-04-21 | 支付宝(杭州)信息技术有限公司 | Judgment result processing method, query method, device, electronic equipment and system |
| CN110944011A (en) * | 2019-12-16 | 2020-03-31 | 支付宝(杭州)信息技术有限公司 | Joint prediction method and system based on tree model |
| CN111695697A (en) * | 2020-06-12 | 2020-09-22 | 深圳前海微众银行股份有限公司 | Multi-party combined decision tree construction method and device and readable storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 刘西蒙;马建峰;熊金波;李琦;张涛;朱辉;: "云计算环境下基于密文策略的权重属性加密方案", 四川大学学报(工程科学版), no. 06 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113807530B (en) | 2024-02-06 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111428887B (en) | Model training control method, device and system based on multiple computing nodes | |
| CN111199037B (en) | Login method, system and device | |
| CN112784823B (en) | Face image recognition method, face image recognition device, computing equipment and medium | |
| CN112434620B (en) | Scene text recognition method, device, equipment and computer readable medium | |
| CN110738323B (en) | Method and device for establishing machine learning model based on data sharing | |
| CN114006769B (en) | Model training method and device based on transverse federal learning | |
| CN114640533B (en) | Method and device for transmitting message, storage medium and electronic equipment | |
| CN116361121A (en) | Abnormal interface alarm method, device, electronic equipment and computer readable medium | |
| CN110765490A (en) | Method and apparatus for processing information | |
| US10049222B1 (en) | Establishing application trust levels using taint propagation | |
| CN113722738A (en) | Data protection method, device, medium and electronic equipment | |
| CN111786937B (en) | Method, apparatus, electronic device and readable medium for identifying malicious request | |
| CN110705635A (en) | Method and apparatus for generating an isolated forest | |
| CN114499893B (en) | Bidding file encryption and evidence storage method and system based on block chain | |
| US20230418794A1 (en) | Data processing method, and non-transitory medium and electronic device | |
| CN113807530B (en) | Information processing system, method and device | |
| CN111752625B (en) | Method and device for interface mock | |
| CN110781523B (en) | Method and apparatus for processing information | |
| CN113221554A (en) | Text processing method and device, electronic equipment and storage medium | |
| CN115730324A (en) | Business system access method and device, storage medium and electronic equipment | |
| CN116226888B (en) | Power data interactive encryption method, system and equipment based on privacy protection | |
| CN114218994A (en) | Method and apparatus for processing information | |
| CN115829729B (en) | Three-chain architecture-based supply chain financial credit evaluation system and method | |
| CN113128200B (en) | Method and device for processing information | |
| CN116453197A (en) | Face recognition method, device, electronic equipment and computer readable medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |