CN112118366A - Method and device for transmitting face picture data - Google Patents
Method and device for transmitting face picture data Download PDFInfo
- Publication number
- CN112118366A CN112118366A CN202010756665.0A CN202010756665A CN112118366A CN 112118366 A CN112118366 A CN 112118366A CN 202010756665 A CN202010756665 A CN 202010756665A CN 112118366 A CN112118366 A CN 112118366A
- Authority
- CN
- China
- Prior art keywords
- face
- data
- matrix
- training model
- model file
- 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.)
- Pending
Links
Images
Classifications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N1/00—Scanning, transmission or reproduction of documents or the like, e.g. facsimile transmission; Details thereof
- H04N1/44—Secrecy systems
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L63/00—Network architectures or network communication protocols for network security
- H04L63/04—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks
- H04L63/0428—Network architectures or network communication protocols for network security for providing a confidential data exchange among entities communicating through data packet networks wherein the data content is protected, e.g. by encrypting or encapsulating the payload
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/06—Protocols specially adapted for file transfer, e.g. file transfer protocol [FTP]
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/10—Protocols in which an application is distributed across nodes in the network
Abstract
The invention discloses a method and a device for transmitting face picture data, wherein the method comprises the following steps: acquiring a face recognition training model file issued by a cloud server through a network route; acquiring face picture data through front-end equipment, and analyzing the face picture data through the face recognition training model file to obtain structured face data; and sending the structured face data to the cloud server. Through this technical scheme, can avoid revealing of picture data, reduce the bandwidth pressure in high in the clouds simultaneously.
Description
Technical Field
The present invention relates to the field of data transmission technologies, and in particular, to a method and an apparatus for transmitting face picture data.
Background
At present, when face picture recognition is carried out, face picture information is mainly collected through front-end equipment, the face picture information is sent to a cloud server, and the face picture is recognized through the cloud server. The defects of the scheme are as follows: the picture data is not easy to encrypt, and the safety of the picture data cannot be ensured. And the picture data is large, which causes a large bandwidth pressure if the concurrency is large.
Disclosure of Invention
In view of the foregoing problems, the present invention provides a method and a device for transmitting face picture data, which can avoid the leakage of picture data and reduce the bandwidth pressure of the cloud.
According to a first aspect of the embodiments of the present invention, there is provided a method for transmitting face picture data, where the method is used for an edge device, and the method includes:
acquiring a face recognition training model file issued by a cloud server through a network route;
acquiring face picture data through front-end equipment, and analyzing the face picture data through the face recognition training model file to obtain structured face data;
and sending the structured face data to the cloud server.
Optionally, the obtaining of the face recognition training model file issued by the cloud server includes:
and acquiring the face recognition training model file issued by the cloud server according to a preset time interval so as to dynamically update the face recognition training model file.
Optionally, the sending the structured face data to the cloud server includes:
encrypting the structured face data to obtain encrypted face data;
and sending the encrypted face data to the cloud server.
Optionally, the face recognition training model file is a binary file.
According to a second aspect of the embodiments of the present invention, there is provided a device for transmitting face picture data, where the device is used for an edge device, the device includes:
the acquisition module is used for acquiring a face recognition training model file issued by the cloud server through a network route;
the analysis module is used for acquiring face picture data through front-end equipment and analyzing the face picture data through the face recognition training model file to obtain structured face data;
and the sending module is used for sending the structured face data to the cloud server.
Optionally, the obtaining module is configured to:
and acquiring the face recognition training model file issued by the cloud server according to a preset time interval so as to dynamically update the face recognition training model file.
Optionally, the sending module includes:
the encryption unit is used for encrypting the structured human face data to obtain encrypted human face data;
and the sending unit is used for sending the encrypted face data to the cloud server.
Optionally, the face recognition training model file is a binary file.
According to a third aspect of the embodiments of the present invention, there is provided a device for transmitting face picture data, which is used for an edge device, and includes:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to:
acquiring a face recognition training model file issued by a cloud server through a network route;
acquiring face picture data through front-end equipment, and analyzing the face picture data through the face recognition training model file to obtain structured face data;
and sending the structured face data to the cloud server.
According to a fourth aspect of embodiments of the present invention, there is provided a computer-readable storage medium having stored thereon computer instructions which, when executed by a processor, implement the steps of the method of the first aspect.
In the embodiment of the invention, the face recognition training model file of the cloud server is routed to the edge terminal device through the network, the edge terminal device dynamically loads the face recognition training model file, acquires the face picture data for analysis, and finally encrypts the structured data and sends the encrypted data to the cloud server, so that the face recognition model under an AI scene can be effectively prevented from being stolen, the leakage of the picture data is avoided, and the bandwidth pressure of the cloud is reduced.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a flowchart of a method for transmitting face picture data according to an embodiment of the present invention.
Fig. 2 is a flowchart of another method for transmitting face picture data according to an embodiment of the present invention.
Fig. 3 is a flowchart of another method for transmitting face picture data according to an embodiment of the present invention.
Fig. 4 is a block diagram of a device for transmitting face picture data according to an embodiment of the present invention.
Fig. 5 is a block diagram of another apparatus for transmitting face picture data according to an embodiment of the present invention.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention.
In some of the flows described in the present specification and claims and in the above figures, a number of operations are included that occur in a particular order, but it should be clearly understood that these operations may be performed out of order or in parallel as they occur herein, with the order of the operations being indicated as 101, 102, etc. merely to distinguish between the various operations, and the order of the operations by themselves does not represent any order of performance. Additionally, the flows may include more or fewer operations, and the operations may be performed sequentially or in parallel. It should be noted that, the descriptions of "first", "second", etc. in this document are used for distinguishing different messages, devices, modules, etc., and do not represent a sequential order, nor limit the types of "first" and "second" to be different.
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a flowchart of a method for transmitting face image data according to an embodiment of the present invention, and as shown in fig. 1, the method for transmitting face image data is used for an edge device, and includes:
step S101, a face recognition training model file issued by a cloud server through a network route is obtained.
Optionally, the face recognition training model file is a binary file.
The face recognition training model file is a binary file, so that even if the model file is stolen, the opposite side cannot be opened, and the safety of data is ensured.
Step S102, collecting face picture data through front-end equipment, and analyzing the face picture data through the face recognition training model file to obtain structured face data.
Step S103, sending the structured human face data to the cloud server.
In the embodiment, the face recognition training model file of the cloud server is routed to the edge end device through the network, the edge end device dynamically loads the face recognition training model file, obtains the face picture data for analysis, and finally encrypts the structured data and sends the encrypted structured data to the cloud server.
In one embodiment, the face picture data is analyzed by the face recognition training model file to obtain the structured face data, including steps a1-a 5:
step A1, for each face picture in the face picture data, performing the following steps A11-A13:
step A11, extracting the features of the current face picture, and establishing a face feature value matrix X of the current face picture according to the extracted face data feature values:
step A12, obtaining a matrix Y corresponding to the current face picture according to the face eigenvalue matrix X of the current face picture:
Y＝(x11，…，x1n，x21，…，x2n，…，xm1，…，xmn)T
step A13, performing normalization processing on the matrix Y corresponding to the current face picture to obtain Z corresponding to the current face picture:
step A2, if there are s human face pictures in the human face picture data, s Z can be obtained according to the steps A11-A13 and recorded as Z1，…，ZsWherein Z isiZ corresponding to the ith human face picture is represented;
step a3, establishing matrix M ═ Z1，…，Zs)T；
Step A4, establishing the following optimization model:
max Tr(WTMMTW)s.t.WTW＝I
wherein, W is a matrix to be solved, and W is an mn × d dimensional real matrix; d is a preset dimension of W formed after dimension reduction of the X and can be set manually; wherein Tr represents the trace of the matrix, and Tr () has the expression
Is a matrix of n x n; s.t. is an abbreviation for subject to, representing "such that", I is the d-dimensional identity matrix;
a5, solving the optimization model by adopting a matrix eigenvalue decomposition method to obtain W; specifically, the steps A51-A53:
step A51, setting formula (1) as follows:
MMT＝P-1AP (1)
wherein, P is an mn × mn dimensional real matrix; Λ is a preset mn × mn dimensional real matrix, Λ ═ diag (λ)1，…，λmn)，λ1、λ2、…λmnIs a preset value; and lambda1≥λ2≥…≥λmn；
A52, solving a formula (1) to obtain a matrix P;
step A53, taking the first d columns of the matrix P to form a matrix W, namely:
W＝(P1，P2，…，Pd)，Piis the ith column of P, i ═ 1,2, … d;
the matrix W is the structured face data.
The beneficial effects of the above technical scheme are: the original face data cannot be directly obtained by a stolen object through the transformation and dimension reduction of the face picture data, so that the safety of the data is enhanced, and meanwhile, the structural face data formed after the dimension reduction can still reflect the face characteristics; in addition, the storage space occupied by the structured face data formed after dimensionality reduction is smaller than that of the original data, the requirement on the storage capacity is reduced, the structured face data can be sent to the cloud server by using smaller bandwidth, and the requirement on the transmission network bandwidth is reduced.
Fig. 2 is a flowchart of another method for transmitting face picture data according to an embodiment of the present invention.
As shown in fig. 2, optionally, the step S101 includes:
step S201, obtaining a face recognition training model file issued by the cloud server according to a preset time interval, so as to dynamically update the face recognition training model file.
In this embodiment, the edge device may dynamically load the face recognition training model file according to a preset time interval to dynamically update the face recognition training model file, thereby ensuring the validity of the model file.
Fig. 3 is a flowchart of another method for transmitting face picture data according to an embodiment of the present invention.
As shown in fig. 3, optionally, the step S103 includes:
step S301, encrypting the structured human face data to obtain the encrypted human face data. The structured data may be structured data in a predetermined format, which includes face feature data.
Step S302, sending the encrypted face data to the cloud server.
In this embodiment, the structured face data is encrypted and then sent to the cloud server, so that the security of the face data can be further ensured.
The above description describes a transmission process of face picture data, which can be implemented by a device, and the internal structure and function of the device are described below.
Fig. 4 is a block diagram of a device for transmitting face picture data according to an embodiment of the present invention.
As shown in fig. 4, according to a second aspect of the embodiments of the present invention, there is provided an apparatus for transmitting face picture data, where the apparatus is used for an edge device, the apparatus includes:
the obtaining module 41 is configured to obtain a face recognition training model file issued by a cloud server through a network route;
the analysis module 42 is configured to acquire face image data through a front-end device, and analyze the face image data through the face recognition training model file to obtain structured face data;
a sending module 43, configured to send the structured face data to the cloud server.
Optionally, the obtaining module 41 is configured to:
and acquiring the face recognition training model file issued by the cloud server according to a preset time interval so as to dynamically update the face recognition training model file.
Fig. 5 is a block diagram of another apparatus for transmitting face picture data according to an embodiment of the present invention.
As shown in fig. 5, optionally, the sending module 43 includes:
an encrypting unit 51, configured to encrypt the structured face data to obtain encrypted face data;
a sending unit 52, configured to send the encrypted face data to the cloud server.
Optionally, the face recognition training model file is a binary file.
According to a third aspect of the embodiments of the present invention, there is provided a device for transmitting face picture data, which is used for an edge device, and includes:
a processor;
a memory for storing processor-executable instructions;
wherein the processor is configured to:
acquiring a face recognition training model file issued by a cloud server through a network route;
acquiring face picture data through front-end equipment, and analyzing the face picture data through the face recognition training model file to obtain structured face data;
and sending the structured face data to the cloud server.
The processor is further configured to:
optionally, the obtaining of the face recognition training model file issued by the cloud server includes:
and acquiring the face recognition training model file issued by the cloud server according to a preset time interval so as to dynamically update the face recognition training model file.
Optionally, the sending the structured face data to the cloud server includes:
encrypting the structured face data to obtain encrypted face data;
and sending the encrypted face data to the cloud server.
Optionally, the face recognition training model file is a binary file.
According to a fourth aspect of embodiments of the present invention, there is provided a computer-readable storage medium having stored thereon computer instructions which, when executed by a processor, implement the steps of the method of the first aspect.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (9)
1. A method for transmitting face picture data is used for an edge device, and comprises the following steps:
acquiring a face recognition training model file issued by a cloud server through a network route;
acquiring face picture data through front-end equipment, and analyzing the face picture data through the face recognition training model file to obtain structured face data;
and sending the structured face data to the cloud server.
2. The method of claim 1, wherein the obtaining of the face recognition training model file sent by the cloud server comprises:
and acquiring the face recognition training model file issued by the cloud server according to a preset time interval so as to dynamically update the face recognition training model file.
3. The method of claim 1, wherein sending the structured face data to the cloud server comprises:
encrypting the structured face data to obtain encrypted face data;
and sending the encrypted face data to the cloud server.
4. The method of claim 1, wherein the face recognition training model file is a binary file.
5. The method according to claim 1, wherein said analyzing said face picture data by said face recognition training model file to obtain structured face data comprises steps a1-a 5:
step A1, for each face picture in the face picture data, performing the following steps A11-A13:
step A11, extracting the features of the current face picture, and establishing a face feature value matrix X of the current face picture according to the extracted face data feature values:
step A12, obtaining a matrix Y corresponding to the current face picture according to the face eigenvalue matrix X of the current face picture:
Y＝(x11，…，x1n，x21，…，x2n，…，xm1，…，xmn)T
step A13, performing normalization processing on the matrix Y corresponding to the current face picture to obtain Z corresponding to the current face picture:
step A2, if there are s human face pictures in the human face picture data, s Z can be obtained according to the steps A11-A13 and recorded as Z1，…，ZsWherein Z isiZ corresponding to the ith human face picture is represented;
step a3, establishing matrix M ═ Z1，…，Zs)T；
Step A4, establishing the following optimization model:
maxTr(WTMMTW)s.t.WTW＝I
wherein, W is a matrix to be solved, and W is an mn × d dimensional real matrix; d is a preset dimensionality after dimensionality reduction of the X and can be set manually; wherein Tr represents the trace of the matrix, and Tr () has the expression
X is a matrix of n X n; s.t. is an abbreviation for subject to, representing "such that", I is the d-dimensional identity matrix;
a5, solving the optimization model by adopting a matrix eigenvalue decomposition method to obtain W; specifically, the steps A51-A53:
step A51, setting formula (1) as follows:
MMT＝P-1ΛP (1)
wherein, P is an mn × mn dimensional real matrix; Λ is a preset mn × mn dimensional real matrix, Λ ═ diag (λ)1，…，λmn)，λ1、λ2、…λmmIs a preset value; and lambda1≥λ2≥…≥λmn；
A52, solving a formula (1) to obtain a matrix P;
step A53, taking the first d columns of the matrix P to form a matrix W, namely:
W＝(P1，P2，…，Pd)，Piis the ith column of P, i ═ 1,2, … d;
the matrix W is the structured face data.
6. An apparatus for transmitting face picture data, the apparatus being used for an edge device, the apparatus comprising:
the acquisition module is used for acquiring a face recognition training model file issued by the cloud server through a network route;
the analysis module is used for acquiring face picture data through front-end equipment and analyzing the face picture data through the face recognition training model file to obtain structured face data;
and the sending module is used for sending the structured face data to the cloud server.
7. The apparatus of claim 6, wherein the obtaining module is configured to:
and acquiring the face recognition training model file issued by the cloud server according to a preset time interval so as to dynamically update the face recognition training model file.
8. The apparatus of claim 6, wherein the sending module comprises:
the encryption unit is used for encrypting the structured human face data to obtain encrypted human face data;
and the sending unit is used for sending the encrypted face data to the cloud server.
9. The apparatus of claim 6, wherein the face recognition training model file is a binary file.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010756665.0A CN112118366A (en) | 2020-07-31 | 2020-07-31 | Method and device for transmitting face picture data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010756665.0A CN112118366A (en) | 2020-07-31 | 2020-07-31 | Method and device for transmitting face picture data |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN112118366A true CN112118366A (en) | 2020-12-22 |
Family
ID=73799401
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010756665.0A Pending CN112118366A (en) | 2020-07-31 | 2020-07-31 | Method and device for transmitting face picture data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112118366A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112801134A (en) * | 2020-12-30 | 2021-05-14 | 杭州趣链科技有限公司 | Gesture recognition model training and distributing method and device based on block chain and image |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108805185A (en) * | 2018-05-29 | 2018-11-13 | 腾讯科技（深圳）有限公司 | Training method, device, storage medium and the computer equipment of model |
| CN110378092A (en) * | 2019-07-26 | 2019-10-25 | 北京积加科技有限公司 | Identification system and client, server and method |
| CN110633669A (en) * | 2019-09-12 | 2019-12-31 | 华北电力大学（保定） | Mobile terminal face attribute identification method based on deep learning in home environment |
| CN110909794A (en) * | 2019-11-22 | 2020-03-24 | 乐鑫信息科技（上海）股份有限公司 | Target detection system suitable for embedded equipment |
| CN111275207A (en) * | 2020-02-10 | 2020-06-12 | 深圳前海微众银行股份有限公司 | Semi-supervision-based horizontal federal learning optimization method, equipment and storage medium |
| CN111444848A (en) * | 2020-03-27 | 2020-07-24 | 广州英码信息科技有限公司 | Specific scene model upgrading method and system based on federal learning |
-
2020
- 2020-07-31 CN CN202010756665.0A patent/CN112118366A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108805185A (en) * | 2018-05-29 | 2018-11-13 | 腾讯科技（深圳）有限公司 | Training method, device, storage medium and the computer equipment of model |
| CN110378092A (en) * | 2019-07-26 | 2019-10-25 | 北京积加科技有限公司 | Identification system and client, server and method |
| CN110633669A (en) * | 2019-09-12 | 2019-12-31 | 华北电力大学（保定） | Mobile terminal face attribute identification method based on deep learning in home environment |
| CN110909794A (en) * | 2019-11-22 | 2020-03-24 | 乐鑫信息科技（上海）股份有限公司 | Target detection system suitable for embedded equipment |
| CN111275207A (en) * | 2020-02-10 | 2020-06-12 | 深圳前海微众银行股份有限公司 | Semi-supervision-based horizontal federal learning optimization method, equipment and storage medium |
| CN111444848A (en) * | 2020-03-27 | 2020-07-24 | 广州英码信息科技有限公司 | Specific scene model upgrading method and system based on federal learning |
Non-Patent Citations (1)
| Title |
|---|
| 万晶晶等: "第6章 特征提取", 《人工智能前沿技术丛书 模式识别》 * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112801134A (en) * | 2020-12-30 | 2021-05-14 | 杭州趣链科技有限公司 | Gesture recognition model training and distributing method and device based on block chain and image |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| EP3893170B1 (en) | Federated learning-based model parameter training method, apparatus and device, and medium | |
| CN109002861B (en) | Federal modeling method, device and storage medium | |
| CN110958218B (en) | Data transmission method based on multi-network communication and related equipment | |
| EP4105821A1 (en) | Face liveness detection method, system and apparatus, computer device, and storage medium | |
| CN107886414B (en) | Order combination method and equipment and computer storage medium | |
| CN112163412B (en) | Data verification method and device, electronic equipment and storage medium | |
| CN112395643A (en) | Data privacy protection method and system of neural network | |
| CN112381000A (en) | Face recognition method, device, equipment and storage medium based on federal learning | |
| CN112231309A (en) | Method, device, terminal equipment and medium for removing duplicate of longitudinal federal data statistics | |
| CN111881740A (en) | Face recognition method, face recognition device, electronic equipment and medium | |
| CN115563600A (en) | Data auditing method and device, electronic equipment and storage medium | |
| CN111523679A (en) | Feature binning method, device and readable storage medium | |
| CN113766085B (en) | Image processing method and related device | |
| CN112118366A (en) | Method and device for transmitting face picture data | |
| CN112329757A (en) | Method, device and system for desensitizing acquisition of bill information | |
| CN113762970A (en) | Data processing method and device, computer readable storage medium and computer equipment | |
| CN114329030A (en) | Information processing method and device, computer equipment and storage medium | |
| CN113901502A (en) | Data processing method and device, electronic equipment and storage medium | |
| CN109003190B (en) | Nuclear protection method, computer readable storage medium and terminal equipment | |
| CN113190858A (en) | Image processing method, system, medium and device based on privacy protection | |
| CN111221803A (en) | Characteristic library management method and coprocessor | |
| CN116383391B (en) | Text classification method and device | |
| CN113537361B (en) | Cross-sample feature selection method in federal learning system and federal learning system | |
| CN114553684B (en) | Method, device, computer equipment and storage medium for network point operation and maintenance | |
| CN115396222B (en) | Device instruction execution method, system, electronic device and readable storage 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 | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20201222 |