CN113434906A - Data query method and device, computer equipment and storage medium - Google Patents
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Abstract
The embodiment of the application belongs to the field of information security, and relates to a data query method, a data query device, computer equipment and a storage medium, wherein the method comprises the following steps: acquiring a first object identifier stored in a local node; determining a common object identifier, namely an object identifier of an intersection object of each node according to a key protocol algorithm; the object data corresponding to the common object identification in the local nodes are first candidate query data; calculating first encrypted data of each node according to the first candidate query data; sending first encrypted data to each node and receiving the first encrypted data sent by each node; calculating second encrypted data according to each first encrypted data of the local node, and receiving the second encrypted data sent by each node; and performing data decryption on each second encrypted data to obtain the object data statistical information of the intersection object in each node. In addition, the present application also relates to a blockchain technique, and object data can be stored in the blockchain. The data query method and the data query device realize data query under the condition that object data privacy is not exposed.
Description
Technical Field
The present application relates to the field of information security technologies, and in particular, to a data query method and apparatus, a computer device, and a storage medium.
Background
Data query is a very popular and widespread technology, in which a query party is provided with contents to be queried by a data holder, the data holder performs retrieval and statistics in a database, and then returns results to the query party.
Today, object data of objects is often stored in different places. For example, in the financial field, user data of users are often stored in different companies, and different companies have the same user but hold different user data. Companies often want to perform data mining in a joint statistics mode, and data value is fully utilized; meanwhile, companies do not want to expose user data of non-intersecting users of each family in joint statistics, nor do companies want to expose specific values of own user data. Moreover, with the standardization of data privacy protection, data circulation between different companies cannot be performed at will, which makes it impossible to perform data query without exposing object data.
Disclosure of Invention
An embodiment of the application aims to provide a data query method, a data query device, computer equipment and a storage medium, so as to solve the problem that object data are exposed during data query.
In order to solve the above technical problem, an embodiment of the present application provides a data query method, which adopts the following technical solutions:
acquiring a first object identifier stored in a local node;
determining the intersection of the first object identifier and a second object identifier according to a preset key protocol algorithm to obtain a common object identifier, wherein the second object identifier is an object identifier stored in each node in the cluster;
determining object data corresponding to the common object identifiers in the local nodes as first candidate query data;
calculating first encrypted data corresponding to each node according to the first candidate query data and the number of the nodes;
sending first encrypted data corresponding to the nodes, and receiving the first encrypted data corresponding to the local nodes sent by the nodes;
calculating second encrypted data of the local node according to each first encrypted data corresponding to the local node, and receiving the second encrypted data sent by each node;
and performing data decryption on each second encrypted data to obtain data query information, wherein the data query information is object data statistical information of the intersection object in each node.
In order to solve the above technical problem, an embodiment of the present application further provides a data query device, which adopts the following technical solutions:
the identification acquisition module is used for acquiring a first object identification stored in a local node;
the intersection determining module is used for determining the intersection of the first object identifier and the second object identifier according to a preset key protocol algorithm to obtain a common object identifier, wherein the second object identifier is an object identifier stored in each node in the cluster;
a data determining module, configured to determine, as first candidate query data, object data corresponding to the common object identifier in the local node;
the first calculation module is used for calculating first encrypted data corresponding to each node according to the first candidate query data and the number of the nodes;
the sending and receiving module is used for sending first encrypted data corresponding to the nodes and receiving the first encrypted data corresponding to the local nodes sent by the nodes;
the second calculation module is used for calculating second encrypted data of the local node according to each first encrypted data corresponding to the local node and receiving the second encrypted data sent by each node;
and the data reconstruction module is used for carrying out data decryption on each second encrypted data to obtain data query information, wherein the data query information is object data statistical information of intersection objects in each node.
In order to solve the above technical problem, an embodiment of the present application further provides a computer device, which adopts the following technical solutions:
acquiring a first object identifier stored in a local node;
determining the intersection of the first object identifier and a second object identifier according to a preset key protocol algorithm to obtain a common object identifier, wherein the second object identifier is an object identifier stored in each node in the cluster;
determining object data corresponding to the common object identifiers in the local nodes as first candidate query data;
calculating first encrypted data corresponding to each node according to the first candidate query data and the number of the nodes;
sending first encrypted data corresponding to the nodes, and receiving the first encrypted data corresponding to the local nodes sent by the nodes;
calculating second encrypted data of the local node according to each first encrypted data corresponding to the local node, and receiving the second encrypted data sent by each node;
and performing data decryption on each second encrypted data to obtain data query information, wherein the data query information is object data statistical information of the intersection object in each node.
In order to solve the above technical problem, an embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solutions:
acquiring a first object identifier stored in a local node;
determining the intersection of the first object identifier and a second object identifier according to a preset key protocol algorithm to obtain a common object identifier, wherein the second object identifier is an object identifier stored in each node in the cluster;
determining object data corresponding to the common object identifiers in the local nodes as first candidate query data;
calculating first encrypted data corresponding to each node according to the first candidate query data and the number of the nodes;
sending first encrypted data corresponding to the nodes, and receiving the first encrypted data corresponding to the local nodes sent by the nodes;
calculating second encrypted data of the local node according to each first encrypted data corresponding to the local node, and receiving the second encrypted data sent by each node;
and performing data decryption on each second encrypted data to obtain data query information, wherein the data query information is object data statistical information of the intersection object in each node.
Compared with the prior art, the embodiment of the application mainly has the following beneficial effects: acquiring a first object identifier in a local node, processing the first object identifier according to a key protocol algorithm, and determining an intersection object of each node under the condition of not exposing the specific first object identifier to obtain a common object identifier; the method comprises the steps that object data corresponding to common object identifications in local nodes are first candidate query data in joint query, and the first candidate query data and the number of nodes are calculated in an encryption calculation mode during joint query to obtain respective first encryption data of each node; sending first encrypted data corresponding to the nodes to each node, and receiving the first encrypted data corresponding to the local nodes sent by each node; calculating second encrypted data of the local node according to the first encrypted data of the local node, and receiving the second encrypted data sent by each node; and then, carrying out data decryption on each second encrypted data according to a preset decryption mode to obtain data query information, wherein the data query information is object data statistical information of an intersection object in each node, and in the joint query process, each node breaks a data barrier and realizes data query under the condition of not exposing specific private data.
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In order to more clearly illustrate the solution of the present application, the drawings needed for describing the embodiments of the present application will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and that other drawings can be obtained by those skilled in the art without inventive effort.
FIG. 1 is an exemplary system architecture diagram in which the present application may be applied;
FIG. 2 is a flow diagram of one embodiment of a data query method according to the present application;
FIG. 3 is a schematic block diagram of one embodiment of a data query device according to the present application;
FIG. 4 is a schematic block diagram of one embodiment of a computer device according to the present application.
Detailed Description
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs; the terminology used in the description of the application herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application; the terms "including" and "having," and any variations thereof, in the description and claims of this application and the description of the above figures are intended to cover non-exclusive inclusions. The terms "first," "second," and the like in the description and claims of this application or in the above-described drawings are used for distinguishing between different objects and not for describing a particular order.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
In order to make the technical solutions better understood by those skilled in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings.
As shown in fig. 1, the system architecture 100 may include terminal devices 101, 102, 103, a network 104, and servers 105, 106, 107. The network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the servers 105, 106, 107. Network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the terminal devices 101, 102, 103 to interact with the servers 105, 106, 107 via the network 104 to receive or send messages or the like. The terminal devices 101, 102, 103 may have various communication client applications installed thereon, such as a web browser application, a shopping application, a search application, an instant messaging tool, a mailbox client, social platform software, and the like.
The terminal devices 101, 102, 103 may be various electronic devices having a display screen and supporting web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 players (Moving Picture experts Group Audio Layer III, mpeg compression standard Audio Layer 3), MP4 players (Moving Picture experts Group Audio Layer IV, mpeg compression standard Audio Layer 4), laptop portable computers, desktop computers, and the like.
The servers 105, 106, 107 may be servers providing various services, such as background servers providing support for pages displayed on the terminal devices 101, 102, 103.
It should be noted that, the data query method provided in the embodiments of the present application is generally executed by a server, and accordingly, the data query apparatus is generally disposed in the server. In the present application, the server 105 is mainly described, and the local node operates in the server 105, and the nodes also operate in the servers 106 and 107. To distinguish the server 105 from the servers 106, 107, the server 105 is described herein as a "local server".
It should be understood that the number of terminal devices, networks, and servers in fig. 1 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
With continued reference to FIG. 2, a flow diagram of one embodiment of a data query method in accordance with the present application is shown. The data query method comprises the following steps:
in step S201, a first object identifier stored in a local node is obtained.
In this embodiment, the electronic device (for example, the server shown in fig. 1) on which the data query method operates may communicate through a wired connection or a wireless connection. It should be noted that the wireless connection means may include, but is not limited to, a 3G/4G connection, a WiFi connection, a bluetooth connection, a WiMAX connection, a Zigbee connection, a uwb (ultra wideband) connection, and other wireless connection means now known or developed in the future.
The first object identifier may be an identifier of an object, and specifically may be an identifier of an object stored in the local node. The object data of the objects can be stored in different nodes, each node stores the object data of a certain number of objects, and the objects in each node may have intersection; for an intersection object, the object data of the intersection object may be different for different nodes. For example, in a financial marketing scenario, the object may be a user and the first object identification may be a user identification, such as a user name, a user number, and the like. The nodes can be servers of different subsidiaries under the same company, the users consume in the different subsidiaries, and the servers of all the subsidiaries store part of the consumption amount of the users.
By the data query method, the query and statistics of the object data are realized under the condition that each node does not expose specific object data.
When the local server needs to perform the joint data query, the first object identifier stored in the local node is obtained first.
Step S202, determining the intersection of the first object identifier and the second object identifier according to a preset key protocol algorithm to obtain a common object identifier, wherein the second object identifier is an object identifier stored in each node in the cluster.
Wherein the second object identification may be an identification of an object, the second object identification being stored in other nodes in the cluster.
The key agreement algorithm is an algorithm for ensuring the safety of the two communication parties to determine the symmetric key. The first object identifier and the second object identifier are also data privacy, and in order not to expose the existing object identifiers of the nodes, the local node and the nodes can calculate according to a key agreement algorithm, so that a common object identifier is obtained. The common object identifier is the object identifier of the intersection object shared by all the nodes.
Further, the step S202 may include:
step S2021, rank each node in the cluster to obtain a node sequence, and set a first node in the node sequence as an intersection solving node.
Specifically, the local server may sort the nodes in the cluster to obtain a node sequence. The nodes have node identifiers, which may be numbers, and the nodes may be sorted according to the node identifiers, or randomly sorted.
To compute orderly, the first node in the sequence of nodes may be set as the intersection finding node. The intersection solving node is a node which is required to determine an intersection object with the local node currently.
Step S2022, calculating the first object identifier in the local node and the second object identifier of the intersection solution node according to a preset key protocol algorithm to determine an intermediate common object identifier, where the intermediate common object identifier is an object identifier of an intersection object of the local node and the intersection solution node.
Specifically, the local node in the local server may calculate the first object identifier according to a preset key protocol algorithm to obtain a first calculation result, and the intersection solution node also calculates the second object identifier according to the preset key protocol algorithm to obtain a second calculation result, so that the object identifiers of the intersection objects in the local node and the intersection solution node are determined based on the first calculation result and the second calculation result to obtain the middle common object identifier.
Further, the step S2022 may include: taking a first object identifier of a local node as an original root, and calculating the original root through a Diffie Hellman key protocol algorithm to obtain a first secret key; receiving a second secret key sent by the intersection solving node, wherein the second secret key is obtained by calculating a second object identifier in the intersection solving node by the intersection solving node according to a Diffie Hellman key protocol algorithm; comparing the first secret key with the second secret key to determine the same secret key; a first object identification corresponding to the same secret key is determined as the intermediate common object identification.
Diffie Hellman is an algorithm for ensuring a shared secret key to safely traverse an insecure network, and two parties needing secure communication can determine a symmetric secret key through the algorithm and then encrypt and decrypt the secret key. The Diffie Hellman key protocol algorithm can only be used for the exchange of keys and cannot encrypt and decrypt messages.
Specifically, the same object identification in each node is determined by the principle of a Diffie Hellman key protocol algorithm under the condition that the object identification of each node is not exposed.
Based on the principle of Diffie Hellman key protocol algorithm, the local server randomly selects and discloses a prime number p, and when calculating, the first object identifier is used as the original root of the prime number p. For each first object identification, the local server randomly selects a random number a smaller than the prime number p and calculates a first public key A as hash (x)amodp, where hash represents a hash operation, x is the first object identifier in the local node, and mod represents a remainder operation. The local server changes the first public key A to hash (x)amodp is sent to the intersection solving node.
The second object mark in the intersection solving node is y, the intersection solving node randomly selects a random number B smaller than the prime number p, and calculates a second public key B as hash (y)bmodp, then change the second public key B to hash (y)bmodp is sent to the local node.
The local node receives a second public key B which is sent by the intersection solving node and is hash (y)bmodp, the first secret key Ba ═ B is calculatedamodp; the intersection solving node receives a first public key A which is sent by the local node and is hash (x)amod p, then a second secret key Ab ═ A is calculatedbmodp, then the second secret key Ab ═ Abmodp is sent to the local node.
The server compares each first secret key with each second secret key, when the number of a certain first secret key is equal to that of a certain second secret key, the first object identifier and the second object identifier of the two secret keys are also equal according to a Diffie Hellman key protocol algorithm, and therefore a common object can be determined, and a middle common object identifier is obtained.
In the embodiment, the first object identifier is used as an original root, the secret key is calculated according to the original root through a Diffie Hellman key protocol algorithm, and whether the secret key is the same or not is compared, so that whether the first object identifier is the same as the second object identifier or not can be determined, and the common object can be determined under the condition that the specific object identifier is not disclosed.
Step S2023, setting the next node in the node sequence as an intersection solving node, and performing iterative computation on the middle common object identifier and the second object identifier of the intersection solving node according to a key protocol algorithm until the last node in the node sequence to obtain a common object identifier, wherein the common object identifier is the object identifier of the intersection object of each node.
Specifically, since the object identifiers of all node intersection objects in the cluster are to be obtained, on the basis of the obtained intermediate common object identifier, the calculation with the nodes in the node sequence needs to be continued.
The local node can set the next node in the node sequence as a new intersection solving node, then calculates the middle common object identifier and the second object identifier of the intersection solving node according to the key protocol algorithm to obtain a new middle common object identifier, and when the calculation is finished with the last node in the node sequence, the common object identifier can be obtained, the common object identifier is the object identifier of the intersection object of each node, and each node stores the object data of the object corresponding to the common object identifier.
In this embodiment, the nodes in the cluster are ordered to obtain a node sequence, and then the node sequence is calculated according to the key protocol algorithm and each node in the node sequence, so that the intersection object of all the nodes can be determined, and a common object identifier is obtained.
Step S203, determining object data corresponding to the common object identifier in the local node as first candidate query data.
Specifically, after the common object identifier is obtained, the local server reads the object data stored in the local node, and determines the object data corresponding to the common object identifier as the first candidate query data. For other nodes, object data corresponding to the stored common object identifiers may be determined as second candidate query data. The first candidate query data and the second candidate query data will participate in the federated data query.
It is emphasized that, to further ensure the privacy and security of the object data, the object data may also be stored in a node of a blockchain.
The block chain referred by the application is a novel application mode of computer technologies such as distributed data storage, point-to-point transmission, a consensus mechanism, an encryption algorithm and the like. A block chain (Blockchain), which is essentially a decentralized database, is a series of data blocks associated by using a cryptographic method, and each data block contains information of a batch of network transactions, so as to verify the validity (anti-counterfeiting) of the information and generate a next block. The blockchain may include a blockchain underlying platform, a platform product service layer, an application service layer, and the like.
Step S204, calculating first encrypted data corresponding to each node according to the first candidate query data and the number of the nodes.
Specifically, when the first candidate query data and the second candidate query data are queried and counted, multiple rounds of calculation need to be performed, and first, the local server needs to calculate first encrypted data corresponding to each node according to the first candidate query data and the number of nodes participating in joint query in the cluster. And for each node in the cluster, the local server calculates the first encrypted data corresponding to each node according to the second candidate query data in the local node and the number of the nodes participating in the joint query.
Further, the step S204 may include: determining a first encryption data calculation mode according to the number of the nodes; and substituting the node serial number of each node and the first candidate query data into a first encrypted data calculation mode to obtain first encrypted data corresponding to each node.
Specifically, the first encrypted data has a general algorithm formula, and when the first encrypted data is applied, the general algorithm formula needs to be adjusted according to the number of nodes to obtain an adaptive first encrypted data calculation mode. In one embodiment, the algorithm formula common to the first encrypted data is as follows:
f(x)＝s+a1x+a2x2+...+an-1xn-1(modp) (1)
where s is the first candidate query data stored in the local node, aiI ∈ 1, 2., n-1 is a random number, n is the number of nodes participating in the joint query, x is the node sequence number of each node, and in one embodiment, the node sequence number may form a natural number sequence starting from 1; p is a prime number randomly selected and disclosed by the local server.
In application, the specific form of f (x) is adjusted according to the number of nodes to obtain a first encrypted data calculation mode, for example, when the number of nodes n is 3, the first encrypted data calculation mode is as follows:
f(x)＝s+a1x+a2x2modp (2)
when the local node calculates the first encrypted data, the node serial number of each node and the first candidate query data are substituted into formula (2), and if the local node serial number is 1 and the node serial numbers of the other two nodes (called as a second node and a third node) are 2 and 3, the first encrypted data f (1) corresponding to the local node is obtained through calculation, the first encrypted data corresponding to the second node is f (2), and the first encrypted data corresponding to the third node is f (3).
In this embodiment, a first encryption data calculation mode is determined according to the number of nodes, and then the node number and the first candidate query data are substituted for calculation, so that encryption of the first candidate query data is realized.
Step S205, sending first encrypted data corresponding to the node to each node, and receiving the first encrypted data corresponding to the local node sent by each node.
Specifically, the local server leaves first encrypted data corresponding to the local node, and sends the first encrypted data corresponding to the local node to each node. Meanwhile, other nodes can calculate first encrypted data corresponding to each node according to second data to be inquired of the other nodes, and send the calculated first encrypted data to the corresponding nodes. The local node receives first encrypted data corresponding to the local node and sent by other nodes.
In response to the foregoing example of the present application, the local server transmits the first encrypted data f (2) to the second node, and transmits the first encrypted data f (3) to the third node. The second node calculates first encrypted data g (1), g (2) and g (3), and transmits the first encrypted data g (1) to the local node and the first encrypted data g (3) to the third node. The third node calculates the first encrypted data d (1), d (2) and d (3), and transmits the first encrypted data d (1) to the local node and the second encrypted data d (2) to the second node. The algorithm formula g (x) used by the second node for calculating the first encrypted data is the same as the algorithm formula d (x) used by the third node for calculating the first encrypted data, and the difference is that the candidate query data brought in during calculation is the second candidate query data of each node.
Step S206, calculating second encrypted data of the local node according to each first encrypted data corresponding to the local node, and receiving the second encrypted data sent by each node.
Specifically, after obtaining each first encrypted data corresponding to the local node, the local server calculates each first encrypted data according to a preset calculation mode, so as to obtain a second encrypted data of the local node.
Each node calculates the received first encrypted data according to the same calculation mode to obtain second encrypted data corresponding to the node, and then sends the second encrypted data to the local node.
Further, the step S206 may include: performing linear operation on each first encrypted data corresponding to the local node to obtain second encrypted data of the local node; and receiving second encrypted data which are sent by each node and correspond to the nodes.
Specifically, the preset calculation mode may be linear operation, and the local server performs linear operation on each first encrypted data corresponding to the local node. Each node also performs the same linear operation on each corresponding first encrypted data to obtain respective second encrypted data of each node, and then sends the second encrypted data to the local node.
In one embodiment, the local server adds the first encrypted data corresponding to the local node to obtain the second encrypted data of the local node. For example, in the example of the present application, if the local server already has the first encrypted data f (1), g (1), and d (1), the second encrypted data h (1) is calculated as f (1) + g (1) + d (1); if the second node already has the first encrypted data f (2), g (2) and d (2), calculating second encrypted data h (2) as f (2) + g (2) + d (2); the third node already has the first encrypted data f (3), g (3), and d (3), and then calculates the second encrypted data h (3) as f (3) + g (3) + d (3).
In this embodiment, linear operation may be performed on the first encrypted data to obtain second encrypted data, and the second encrypted data sent by each node is received to prepare data for data query and statistics.
Step S207, performing data decryption on each second encrypted data to obtain data query information, where the data query information is object data statistical information of an intersection object in each node.
Specifically, after the local server obtains the second encrypted data of each node, the second encrypted data is calculated according to a preset calculation mode, the calculation process is a decryption process of the encrypted data, and the calculation result is the data query information. The data query information is a statistical result of the object data of the intersection object in each node. For example, when the first candidate query data is the consumption amount of the user at a certain subsidiary company, the second candidate query data is the consumption amount of the user at other subsidiary companies, and the data query information may be the sum of the consumption amounts of the user at the subsidiary companies.
Further, the step S207 may include: performing data reconstruction on each second encrypted data according to a preset data reconstruction algorithm to obtain decrypted data; and determining the decrypted data as data query information, wherein the data query information is object data statistical information of an intersection object in each node.
Specifically, the local server may calculate each second encrypted data according to a preset data reconstruction algorithm, where the calculation process is a data reconstruction process, that is, a decryption process. The preset data reconstruction algorithm formula is as follows:
where α is a node number, and bears the example of the present application, the local server holds (α, h (α)), and α is 1,2, 3; p is a prime number randomly selected and disclosed by the local server.
Based on the Shamir secret sharing principle, the decrypted data can be obtained according to the formula (3), and the decrypted data is data query information, namely object data statistical information of intersection objects in each node. For example, based on the Shamir secret sharing principle, h (0) ═ s + v + c, where h (0) is data query information, s is first candidate query data in the local node, and v and c are second candidate query data in the second node and the third node, respectively.
In this embodiment, data reconstruction is performed on each second encrypted data according to a preset data reconstruction algorithm, data decryption is achieved, and data query information can be obtained, so that query statistics of object data is achieved without exposing private data.
In this embodiment, a first object identifier in a local node is obtained, the first object identifier is processed according to a key protocol algorithm, and an intersection object of each node can be determined without exposing a specific first object identifier, so as to obtain a common object identifier; the method comprises the steps that object data corresponding to common object identifications in local nodes are first candidate query data in joint query, and the first candidate query data and the number of nodes are calculated in an encryption calculation mode during joint query to obtain respective first encryption data of each node; sending first encrypted data corresponding to the nodes to each node, and receiving the first encrypted data corresponding to the local nodes sent by each node; calculating second encrypted data of the local node according to the first encrypted data of the local node, and receiving the second encrypted data sent by each node; and then, carrying out data decryption on each second encrypted data according to a preset decryption mode to obtain data query information, wherein the data query information is object data statistical information of an intersection object in each node, and in the joint query process, each node breaks a data barrier and realizes data query under the condition of not exposing specific private data. .
Further, after step S207, the method may further include: and sending the data query information to the request node according to the received query information acquisition request sent by the request node.
The query information obtaining request may be a request for data query information from the local node. The node sending the query information acquisition request is the request node.
In particular, other nodes in the cluster may obtain data query information from the local node. When other nodes in the cluster need to acquire data query information from the local node, the node serves as a request node and sends a query information acquisition request to the local server. And the local server verifies the identity of the request node, and after the request node is verified, the local server sends the data query information to the request node.
In one embodiment, data query permissions may be assigned to some nodes in the cluster, such as local nodes in the present application. When a node without data query authority in the cluster needs to perform data query, the node with the data query authority can perform data query, and then data query information is acquired from the node with the data query authority.
In this embodiment, the data query information may be sent to other nodes, so that information sharing is achieved.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and can include the processes of the embodiments of the methods described above when the computer program is executed. The storage medium may be a non-volatile storage medium such as a magnetic disk, an optical disk, a Read-Only Memory (ROM), or a Random Access Memory (RAM).
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and may be performed in other orders unless explicitly stated herein. Moreover, at least a portion of the steps in the flow chart of the figure may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
With further reference to fig. 3, as an implementation of the method shown in fig. 2, the present application provides an embodiment of a data query apparatus, which corresponds to the embodiment of the method shown in fig. 2, and which is particularly applicable to various electronic devices.
As shown in fig. 3, the data query apparatus 300 according to the present embodiment includes: an identification acquisition module 301, an intersection determination module 302, a data determination module 303, a first calculation module 304, a sending and receiving module 305, a second calculation module 306, and a data reconstruction module 307, wherein:
an identifier obtaining module 301, configured to obtain a first object identifier stored in a local node.
The intersection determining module 302 is configured to determine an intersection of the first object identifier and the second object identifier according to a preset key protocol algorithm, so as to obtain a common object identifier, where the second object identifier is an object identifier stored in each node in the cluster.
The data determining module 303 is configured to determine, as the first candidate query data, object data corresponding to the common object identifier in the local node.
The first calculating module 304 is configured to calculate first encrypted data corresponding to each node according to the first candidate query data and the number of nodes.
The sending and receiving module 305 is configured to send first encrypted data corresponding to the node to each node, and receive first encrypted data corresponding to the local node sent by each node.
The second calculating module 306 is configured to calculate second encrypted data of the local node according to each first encrypted data corresponding to the local node, and receive the second encrypted data sent by each node.
And the data reconstructing module 307 is configured to perform data decryption on each second encrypted data to obtain data query information, where the data query information is object data statistical information of an intersection object in each node.
In this embodiment, a first object identifier in a local node is obtained, the first object identifier is processed according to a key protocol algorithm, and an intersection object of each node can be determined without exposing a specific first object identifier, so as to obtain a common object identifier; the method comprises the steps that object data corresponding to common object identifications in local nodes are first candidate query data in joint query, and the first candidate query data and the number of nodes are calculated in an encryption calculation mode during joint query to obtain respective first encryption data of each node; sending first encrypted data corresponding to the nodes to each node, and receiving the first encrypted data corresponding to the local nodes sent by each node; calculating second encrypted data of the local node according to the first encrypted data of the local node, and receiving the second encrypted data sent by each node; and then, carrying out data decryption on each second encrypted data according to a preset decryption mode to obtain data query information, wherein the data query information is object data statistical information of an intersection object in each node, and in the joint query process, each node breaks a data barrier and realizes data query under the condition of not exposing specific private data.
In some optional implementations of this embodiment, the intersection determination module 302 may include: the node sorting submodule, the identification calculation submodule and the iterative calculation submodule, wherein:
and the node sequencing submodule is used for sequencing all the nodes in the cluster to obtain a node sequence, and setting a first node in the node sequence as an intersection solving node.
And the identifier calculation submodule is used for calculating the first object identifier in the local node and the second object identifier of the intersection solving node according to a preset key protocol algorithm so as to determine an intermediate common object identifier, wherein the intermediate common object identifier is the object identifier of the intersection object of the local node and the intersection solving node.
And the iterative computation submodule is used for setting the next node in the node sequence as an intersection solving node, and performing iterative computation on the middle common object identifier and the second object identifier of the intersection solving node according to a key protocol algorithm until the last node in the node sequence to obtain a common object identifier, wherein the common object identifier is the object identifier of the intersection object of each node.
In this embodiment, the nodes in the cluster are ordered to obtain a node sequence, and then the node sequence is calculated according to the key protocol algorithm and each node in the node sequence, so that the intersection object of all the nodes can be determined, and a common object identifier is obtained.
In some optional implementations of this embodiment, the identifier calculation sub-module may include: the device comprises a first calculating unit, a second calculating unit, a key comparison unit and an identification determining unit, wherein:
and the first calculation unit is used for calculating the original root by using the first object identifier of the local node as the original root through a Diffie Hellman key protocol algorithm to obtain a first secret key.
And the second calculation unit is used for receiving a second secret key sent by the intersection calculation node, and the second secret key is obtained by calculating a second object identifier in the intersection calculation node according to a Diffie Hellman key protocol algorithm for the intersection calculation node.
And the key comparison unit is used for comparing the first secret key with the second secret key so as to determine the same secret key.
And the identification determining unit is used for determining the first object identification corresponding to the same secret key as the middle shared object identification.
In the embodiment, the first object identifier is used as an original root, the secret key is calculated according to the original root through a Diffie Hellman key protocol algorithm, and whether the secret key is the same or not is compared, so that whether the first object identifier is the same as the second object identifier or not can be determined, and the common object can be determined under the condition that the specific object identifier is not disclosed.
In some optional implementations of this embodiment, the first calculating module 304 may include: a mode determination submodule and a first computation submodule, wherein:
and the mode determining submodule is used for determining a first encryption data calculation mode according to the number of the nodes.
And the first calculation submodule is used for substituting the node serial number of each node and the first candidate query data into the first encrypted data calculation mode to obtain first encrypted data corresponding to each node.
In this embodiment, a first encryption data calculation mode is determined according to the number of nodes, and then the node number and the first candidate query data are substituted for calculation, so that encryption of the first candidate query data is realized.
In some optional implementations of this embodiment, the second calculating module 306 may include: a linear operation sub-module and a second receiving sub-module, wherein:
and the linear operation submodule is used for performing linear operation on each first encrypted data corresponding to the local node to obtain second encrypted data of the local node.
And the second receiving submodule is used for receiving second encrypted data which are sent by each node and correspond to the nodes.
In this embodiment, linear operation may be performed on the first encrypted data to obtain second encrypted data, and the second encrypted data sent by each node is received to prepare data for data query and statistics.
In some optional implementations of this embodiment, the data reconstructing module 307 may include: a data reconstruction sub-module and a decryption determination sub-module, wherein:
and the data reconstruction submodule is used for performing data reconstruction on each second encrypted data according to a preset data reconstruction algorithm to obtain decrypted data.
And the decryption determining submodule is used for determining the decrypted data as data query information, and the data query information is object data statistical information of the intersection objects in each node.
In this embodiment, data reconstruction is performed on each second encrypted data according to a preset data reconstruction algorithm, data decryption is achieved, and data query information can be obtained, so that query statistics of object data is achieved without exposing private data.
In some optional implementation manners of this embodiment, the data query apparatus 300 may further include an information sending module, where the information sending module is configured to: and sending the data query information to the request node according to the received query information acquisition request sent by the request node.
In this embodiment, the data query information may be sent to other nodes, so that information sharing is achieved.
In order to solve the technical problem, an embodiment of the present application further provides a computer device. Referring to fig. 4, fig. 4 is a block diagram of a basic structure of a computer device according to the present embodiment.
The computer device 4 comprises a memory 41, a processor 42, a network interface 43 communicatively connected to each other via a system bus. It is noted that only computer device 4 having components 41-43 is shown, but it is understood that not all of the shown components are required to be implemented, and that more or fewer components may be implemented instead. As will be understood by those skilled in the art, the computer device is a device capable of automatically performing numerical calculation and/or information processing according to a preset or stored instruction, and the hardware includes, but is not limited to, a microprocessor, an Application Specific Integrated Circuit (ASIC), a Programmable Gate Array (FPGA), a Digital Signal Processor (DSP), an embedded device, and the like.
The computer device can be a desktop computer, a notebook, a palm computer, a cloud server and other computing devices. The computer equipment can carry out man-machine interaction with a user through a keyboard, a mouse, a remote controller, a touch panel or voice control equipment and the like.
The memory 41 includes at least one type of readable storage medium including a flash memory, a hard disk, a multimedia card, a card type memory (e.g., SD or DX memory, etc.), a Random Access Memory (RAM), a Static Random Access Memory (SRAM), a Read Only Memory (ROM), an Electrically Erasable Programmable Read Only Memory (EEPROM), a Programmable Read Only Memory (PROM), a magnetic memory, a magnetic disk, an optical disk, etc. In some embodiments, the memory 41 may be an internal storage unit of the computer device 4, such as a hard disk or a memory of the computer device 4. In other embodiments, the memory 41 may also be an external storage device of the computer device 4, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the computer device 4. Of course, the memory 41 may also include both internal and external storage devices of the computer device 4. In this embodiment, the memory 41 is generally used for storing an operating system installed in the computer device 4 and various types of application software, such as computer readable instructions of a data query method. Further, the memory 41 may also be used to temporarily store various types of data that have been output or are to be output.
The processor 42 may be a Central Processing Unit (CPU), controller, microcontroller, microprocessor, or other data Processing chip in some embodiments. The processor 42 is typically used to control the overall operation of the computer device 4. In this embodiment, the processor 42 is configured to execute computer readable instructions stored in the memory 41 or process data, for example, execute computer readable instructions of the data query method.
The network interface 43 may comprise a wireless network interface or a wired network interface, and the network interface 43 is generally used for establishing communication connection between the computer device 4 and other electronic devices.
The computer device provided in this embodiment may execute the data query method described above. The data query method here may be the data query method of the above-described embodiments.
In this embodiment, a first object identifier in a local node is obtained, the first object identifier is processed according to a key protocol algorithm, and an intersection object of each node can be determined without exposing a specific first object identifier, so as to obtain a common object identifier; the method comprises the steps that object data corresponding to common object identifications in local nodes are first candidate query data in joint query, and the first candidate query data and the number of nodes are calculated in an encryption calculation mode during joint query to obtain respective first encryption data of each node; sending first encrypted data corresponding to the nodes to each node, and receiving the first encrypted data corresponding to the local nodes sent by each node; calculating second encrypted data of the local node according to the first encrypted data of the local node, and receiving the second encrypted data sent by each node; and then, carrying out data decryption on each second encrypted data according to a preset decryption mode to obtain data query information, wherein the data query information is object data statistical information of an intersection object in each node, and in the joint query process, each node breaks a data barrier and realizes data query under the condition of not exposing specific private data.
The present application further provides another embodiment, which is to provide a computer-readable storage medium storing computer-readable instructions executable by at least one processor to cause the at least one processor to perform the steps of the data query method as described above.
In this embodiment, a first object identifier in a local node is obtained, the first object identifier is processed according to a key protocol algorithm, and an intersection object of each node can be determined without exposing a specific first object identifier, so as to obtain a common object identifier; the method comprises the steps that object data corresponding to common object identifications in local nodes are first candidate query data in joint query, and the first candidate query data and the number of nodes are calculated in an encryption calculation mode during joint query to obtain respective first encryption data of each node; sending first encrypted data corresponding to the nodes to each node, and receiving the first encrypted data corresponding to the local nodes sent by each node; calculating second encrypted data of the local node according to the first encrypted data of the local node, and receiving the second encrypted data sent by each node; and then, carrying out data decryption on each second encrypted data according to a preset decryption mode to obtain data query information, wherein the data query information is object data statistical information of an intersection object in each node, and in the joint query process, each node breaks a data barrier and realizes data query under the condition of not exposing specific private data.
Through the above description of the embodiments, those skilled in the art will clearly understand that the method of the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but in many cases, the former is a better implementation manner. Based on such understanding, the technical solutions of the present application may be embodied in the form of a software product, which is stored in a storage medium (such as ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (such as a mobile phone, a computer, a server, an air conditioner, or a network device) to execute the method according to the embodiments of the present application.
It is to be understood that the above-described embodiments are merely illustrative of some, but not restrictive, of the broad invention, and that the appended drawings illustrate preferred embodiments of the invention and do not limit the scope of the invention. This application is capable of embodiments in many different forms and is provided for the purpose of enabling a thorough understanding of the disclosure of the application. Although the present application has been described in detail with reference to the foregoing embodiments, it will be apparent to one skilled in the art that the present application may be practiced without modification or with equivalents of some of the features described in the foregoing embodiments. All equivalent structures made by using the contents of the specification and the drawings of the present application are directly or indirectly applied to other related technical fields and are within the protection scope of the present application.
Claims (10)
1. A data query method, comprising the steps of:
acquiring a first object identifier stored in a local node;
determining the intersection of the first object identifier and a second object identifier according to a preset key protocol algorithm to obtain a common object identifier, wherein the second object identifier is an object identifier stored in each node in the cluster;
determining object data corresponding to the common object identifiers in the local nodes as first candidate query data;
calculating first encrypted data corresponding to each node according to the first candidate query data and the number of the nodes;
sending first encrypted data corresponding to the nodes, and receiving the first encrypted data corresponding to the local nodes sent by the nodes;
calculating second encrypted data of the local node according to each first encrypted data corresponding to the local node, and receiving the second encrypted data sent by each node;
and performing data decryption on each second encrypted data to obtain data query information, wherein the data query information is object data statistical information of the intersection object in each node.
2. The data query method of claim 1, wherein the step of determining the intersection of the first object identifier and the second object identifier according to a preset key agreement algorithm to obtain a common object identifier comprises:
sequencing all nodes in the cluster to obtain a node sequence, and setting a first node in the node sequence as an intersection solving node;
calculating a first object identifier in the local node and a second object identifier of the intersection solving node according to a preset key protocol algorithm to determine an intermediate common object identifier, wherein the intermediate common object identifier is an object identifier of an intersection object of the local node and the intersection solving node;
setting the next node in the node sequence as an intersection solving node, and performing iterative computation on the intermediate common object identifier and the second object identifier of the intersection solving node according to the key protocol algorithm until the last node in the node sequence to obtain a common object identifier, wherein the common object identifier is the object identifier of the intersection object of each node.
3. The data query method of claim 2, wherein the step of calculating the first object id in the local node and the second object id of the intersection finding node according to a preset key agreement algorithm to determine an intermediate common object id comprises:
taking the first object identifier of the local node as an original root, and calculating the original root through a Diffie Hellman key protocol algorithm to obtain a first secret key;
receiving a second secret key sent by the intersection solving node, wherein the second secret key is obtained by calculating a second object identifier in the intersection solving node according to the Diffie Hellman key protocol algorithm by the intersection solving node;
comparing the first secret key and the second secret key to determine the same secret key;
and determining the first object identification corresponding to the same secret key as the middle common object identification.
4. The data query method of claim 1, wherein the step of calculating the first encrypted data corresponding to each node according to the first candidate query data and the number of nodes comprises:
determining a first encryption data calculation mode according to the number of the nodes;
and substituting the node serial number of each node and the first candidate query data into the first encrypted data calculation mode to obtain first encrypted data corresponding to each node.
5. The method according to claim 1, wherein the step of calculating second encrypted data of the local node according to each first encrypted data corresponding to the local node and receiving the second encrypted data sent by each node comprises:
performing linear operation on each first encrypted data corresponding to the local node to obtain second encrypted data of the local node;
and receiving second encrypted data which are sent by each node and correspond to the nodes.
6. The data query method according to claim 1, wherein the step of decrypting the second encrypted data to obtain the data query information includes:
performing data reconstruction on each second encrypted data according to a preset data reconstruction algorithm to obtain decrypted data;
and determining the decrypted data as data query information, wherein the data query information is object data statistical information of the intersection objects in each node.
7. The data query method according to claim 1, further comprising, after the step of decrypting the data of each second encrypted data to obtain the data query information:
and sending the data query information to the request node according to a received query information acquisition request sent by the request node.
8. A data query apparatus, comprising:
the identification acquisition module is used for acquiring a first object identification stored in a local node;
the intersection determining module is used for determining the intersection of the first object identifier and the second object identifier according to a preset key protocol algorithm to obtain a common object identifier, wherein the second object identifier is an object identifier stored in each node in the cluster;
a data determining module, configured to determine, as first candidate query data, object data corresponding to the common object identifier in the local node;
the first calculation module is used for calculating first encrypted data corresponding to each node according to the first candidate query data and the number of the nodes;
the sending and receiving module is used for sending first encrypted data corresponding to the nodes and receiving the first encrypted data corresponding to the local nodes sent by the nodes;
the second calculation module is used for calculating second encrypted data of the local node according to each first encrypted data corresponding to the local node and receiving the second encrypted data sent by each node;
and the data reconstruction module is used for carrying out data decryption on each second encrypted data to obtain data query information, wherein the data query information is object data statistical information of intersection objects in each node.
9. A computer device comprising a memory having computer readable instructions stored therein and a processor which when executed implements the steps of the data query method of any one of claims 1 to 7.
10. A computer-readable storage medium, having computer-readable instructions stored thereon, which, when executed by a processor, implement the steps of the data query method of any one of claims 1 to 7.
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