CN116363395A - Verifiable picture similarity recognition method and device based on secret sharing - Google Patents
Verifiable picture similarity recognition method and device based on secret sharing Download PDFInfo
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Abstract
The embodiment of the invention provides a verifiable picture similarity identification method and device based on secret sharing. The method comprises the steps that a first data provider and a second data provider expand picture vectors according to three random numbers, perform slicing and send corresponding slicing to a corresponding computing server; the corresponding calculation server calculates a security parameter pair, sends the security parameter pair to the appointed calculation server, calculates a first security parameter and a second security parameter by the appointed calculation server, and sends the first security parameter and the second security parameter to the first data provider and the second data provider; and the first data provider and the second data provider calculate a third security parameter and a fourth security parameter, and identify the similarity of the pictures. In this way, by adding the verification process, when the verification is successful, it can be proved that both sides send real data on the premise of not exposing privacy, so that the result calculation result of the picture similarity is effective, and the verification of the correctness of the calculation result is also realized.
Description
Technical Field
The present invention relates generally to the field of network security, and more particularly to a verifiable picture similarity recognition method and apparatus based on secret sharing.
Background
In recent years, as mass data increases, the usage of data by people is also increasing. For convenience and rapidity of calculation, people often choose to package data to a third party server for calculation. However, the outsourcing server often has some potential safety hazards, and how to ensure that key data information is not revealed becomes a problem of urgent research. Thus, some means of privacy computation are required to be applied to achieve privacy protection. In machine learning, calculating the similarity of pictures is always an important problem, and how to guarantee the privacy of pictures in the state of outsourcing calculation by a malicious server needs technical means to be realized.
At present, most general calculation based on homomorphic encryption calculates the vector inner product, the calculation efficiency and the communication efficiency are poor, and most vector self is not well applied to a practical scene; if some scenes are basically based on a semi-honest model, parties can mutually try to cheat in a real scene, so that a calculation result is not real.
Disclosure of Invention
According to the embodiment of the invention, a verifiable picture similarity recognition scheme based on secret sharing is provided. According to the scheme, the fact that malicious attacks exist on the servers participating in calculation is considered, through the addition of the verification process, when verification is successful, both sides can prove that real data are sent on the premise that privacy is not exposed, the result calculation result of the picture similarity is effective, and verification of the correctness of the calculation result is achieved.
In a first aspect of the invention, a verifiable picture similarity recognition method based on secret sharing is provided. The method comprises the following steps:
the method comprises the steps that a first data provider generates three random numbers, and the first data provider and a second data provider expand picture vectors according to the three random numbers; slicing the expanded picture vectors of the first data provider and the second data provider, and sending corresponding slices to corresponding computing servers;
the corresponding calculation server calculates a security parameter pair, sends the security parameter pair to a designated calculation server, calculates a first security parameter and a second security parameter by the designated calculation server, sends the first security parameter to the first data provider, and sends the second security parameter to the second data provider;
the first data provider calculates a third security parameter according to the first security parameter, and the second data provider calculates a fourth security parameter according to the second security parameter;
and comparing the third safety parameter and the fourth safety parameter with preset thresholds respectively, and identifying the similarity of the pictures.
Further, the first data provider performs picture vector expansion according to the three random numbers, including:
the first data provider provides a first picture vector, and inserts a first random number into a second random number position in the first picture vector to obtain an expanded first picture vector;
multiplying a third random number by other components except the first random number in the expanded first picture vector to obtain an expanded second picture vector;
and sending the first random number, the second random number and the third random number to a second data provider.
Further, the second data provider performs picture vector expansion according to the three random numbers, including:
the second data provider provides a third picture vector, and inserts the first random number into a second random number position in the third picture vector to obtain an expanded third picture vector; multiplying the third random number with other components except the first random number in the expanded third picture vector to obtain an expanded fourth picture vector.
Further, the slicing the extended picture vector of the first data provider and the second data provider includes:
fragmenting the first picture vector expanded by the first data provider to obtain:
augmenting the first data providerAnd slicing the second picture vector to obtain:
wherein, 
an i-th slice value of the first picture vector extended for the first data provider,>
is a first random number, ">
Is->
The kth item of (a); />
An ith slice value of a second picture vector extended for said first data provider, ->
Is->
The kth item of (a); k is a second random number; />
Is a third random number;
and slicing the third picture vector expanded by the second data provider to obtain:
and slicing the fourth picture vector expanded by the second data provider to obtain:
wherein, 
an ith slice value of the third picture vector extended for the second data provider,/-j>
Is that
The kth item of (a); />
An i-th slice value of a fourth picture vector extended for the second data provider,/>
Is->
The kth item of (a).
Further, the security parameter pair includes:
wherein (1)>
And->
Respectively setting the security parameters in the security parameter pair, wherein i is the current computing server identifier; n is the dimension of the security parameter; />
Is a component in the first picture vector; />
Is a component in the third picture vector; />
Is a security parameter->
Is a first item of (a); />
Is a security parameter->
A second item of (a); />
Is a first random number, ">
Is a security parameter->
K is a second random number; />
Is a security parameter->
The kth item of (a).
Further, the first security parameter is:
the second security parameters are: />
Wherein (1)>
Is a first security parameter; />
Is a second security parameter; />
The parts are slices; />
And w is the counter number.
Further, the first data provider calculates a third security parameter according to the first security parameter, including:
the second data provider calculates a fourth security parameter according to the second security parameter, including:
wherein (1)>
Is a third security parameter; />
Is a fourth security parameter.
Further, comparing the third security parameter and the fourth security parameter with preset thresholds respectively, and identifying the similarity of the pictures includes: if the third security parameter is smaller than a preset threshold, the picture similarity of the first data provider is high; otherwise, the picture similarity of the first data provider is low;
if the fourth security parameter is smaller than a preset threshold, the picture similarity of the second data provider is high; otherwise, the picture similarity of the second data provider is low.
Further, the method further comprises the following steps:
judging
If so, verifying the third safety parameter and the fourth safety parameter, and taking the verified third safety parameter and fourth safety parameter as the safety parameters compared with a preset threshold; otherwise, the verification is not passed.
In a second aspect of the invention, an electronic device is provided. At least one processor of the electronic device; and a memory communicatively coupled to the at least one processor; the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of the first aspect of the invention.
It should be understood that the description in this summary is not intended to limit the critical or essential features of the embodiments of the invention, nor is it intended to limit the scope of the invention. Other features of the present invention will become apparent from the description that follows.
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The above and other features, advantages and aspects of embodiments of the present invention will become more apparent by reference to the following detailed description when taken in conjunction with the accompanying drawings. In the drawings, wherein like or similar reference numerals denote like or similar elements, in which:
FIG. 1 illustrates a flow chart of a verifiable picture similarity identification method based on secret sharing according to an embodiment of the invention;
FIG. 2 illustrates a block diagram of an exemplary electronic device capable of implementing embodiments of the invention;
wherein 200 is an electronic device, 201 is a computing unit, 202 is a ROM, 203 is a RAM, 204 is a bus, 205 is an I/O interface, 206 is an input unit, 207 is an output unit, 208 is a storage unit, 209 is a communication unit.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
In addition, the term "and/or" herein is merely an association relationship describing an association object, and means that three relationships may exist, for example, a and/or B may mean: a exists alone, A and B exist together, and B exists alone. In addition, the character "/" herein generally indicates that the front and rear associated objects are an "or" relationship.
In the present invention, two entities are involved, a data provider and a computing server, respectively.
In the present embodiment, data provision of two encrypted imagesThe parties are a first data provider A and a second data provider B respectively; the number of the computing servers is P, and
and (3) representing. The default picture is in vector form, and the first data provider A holds picture vector +.>
The second data provider B holds a picture vector +.>
. The two parties hope to obtain the inner product of the two vectors through calculation, and compare with the corresponding threshold delta, and judge whether the similarity of the two pictures exceeds the acceptance range on the basis of privacy protection. In consideration of server dislike (i.e. attempting to acquire the privacy information of the picture vector and transmitting false data), the scheme is completed by p computing parties together in order to prevent the problem that a single computing party can learn the privacy of the data provider.
Fig. 1 shows a flowchart of a verifiable picture similarity recognition method based on secret sharing according to an embodiment of the invention.
The method comprises the following steps:
s101, a first data provider generates three random numbers, and the first data provider and a second data provider expand picture vectors according to the three random numbers; and slicing the expanded picture vectors of the first data provider and the second data provider, and sending corresponding slices to corresponding computing servers.
In this embodiment, three random numbers are r, k, respectively,
. The first data provider performs image vector expansion according to the three random numbers, and the method comprises the following steps: said first data provider providing a first picture vector +.>
Inserting a first random number r into said first picture vector +.>
The second random number k position in the image is used for obtaining the expanded first image vector
The method comprises the steps of carrying out a first treatment on the surface of the It can be seen that the first picture vector of n dimensions is extended to n+1 dimensions.
Will third random number
And said extended first picture vector +.>
Divide the first random number +.>
Is multiplied by other components to obtain an extended second picture vector
。
The first random number is added to the first random number
A second random number k and a third random number +.>
To the second data provider B.
In this embodiment, the second data provider B receives the first random number
A second random number k and a third random number +.>
Then, carrying out image vector expansion according to the three random numbers, wherein the method comprises the following steps:
the second data provider B provides a third picture vector
The first random number +.>
Insert into the third picture vector +.>
The second random number k position in the image is used for obtaining the expanded third image vector
The method comprises the steps of carrying out a first treatment on the surface of the Third random number->
And the extended third picture vector
Divide the first random number +.>
Is multiplied by the other components of (a) to obtain an extended fourth picture vector +.>
。
In this embodiment, the slicing the extended picture vector of the first data provider and the second data provider includes:
picture vector extended to the first data provider
Secret slicing, i.e. the first picture vector +.>
Dividing the mixture into p parts according to multiplication to obtain:
second picture vector expanded for the first data provider>
Slicing to obtain:
wherein, 
a first picture vector expanded for the first data provider>
I-th fragment value of +.>
Is a first random number, ">
Is->
The kth item of (a); />
Second picture vector expanded for the first data provider>
I-th fragment value of +.>
Is->
The kth item of (a); k is a second random number; />
Is a third random number.
Then, handle
And->
Send to the i-th computing server +.>
。
A third picture vector extended to the second data provider
Slicing to obtain:
fourth picture vector expanded for the second data provider>
Slicing to obtain:
wherein, 
third picture vector extended for the second data provider +.>
I-th fragment value of +.>
Is that
The kth item of (a); />
Fourth picture vector extended for the second data provider +.>
I-th fragment value of +.>
Is->
The kth item of (a).
Then, handle
And->
Send to the i-th computing server +.>
。
S102, a corresponding calculation server calculates a security parameter pair, sends the security parameter pair to a designated calculation server, calculates a first security parameter and a second security parameter by the designated calculation server, sends the first security parameter to the first data provider, and sends the second security parameter to the second data provider.
In this embodiment, the calculation server that received the decomposition vector calculates the security parameter pair one by one. The security parameter pair comprises:
wherein (1)>
And->
Respectively setting the security parameters in the security parameter pair, wherein i is the current computing server identifier; n is the dimension of the security parameter; />
Is a component in the first picture vector; />
Is a component in the third picture vector; />
Is a security parameter->
Is a first item of (a); />
Is a security parameter->
A second item of (a); />
Is a first random number, ">
Is a security parameter->
K is a second random number; />
Is a security parameter->
The kth item of (a).
Computing server
（i=2/>
3/>
…/>
p) transmitting the self-calculated security parameter pair to a designated computing server. In this embodiment, the first calculation server is specified +.>
A security parameter pair is received.
The first computing server
Calculate the first security parameter +.>
And a second security parameter->
。
The first security parameter
；
The second security parameters are:
wherein (1)>
Is a first security parameter; />
Is a second security parameter; />
The parts are slices; />
And w represents a different counter number. Then, the first security parameter +.>
To said first data provider a, to send said second security parameter +.>
And the data is sent to the second data provider B, so that the calculation and communication efficiency is improved.
S103, the first data provider A is used for providing the first security parameters
Calculate the third security parameter +.>
And said second data provider B being based on said second security parameter +.>
Calculate the fourth security parameter +.>
。
In this embodiment, the first data provider a is configured to provide the first security parameter
Calculating a third security parameter
Comprising: />
Said second data provider B being based on said second security parameter +.>
Calculate the fourth security parameter +.>
Comprising: />
Wherein (1)>
Is a third security parameter; />
Is a fourth security parameter.
As one embodiment of the invention, a third security parameter is calculated
And fourth security parameter->
After that, the fourth security parameter is verified +.>
And->
Whether the two are equal, if so, the verification is passed; otherwise, judging that the computing server has fraud, and verifying the fraud is not passed.
S104, comparing the third safety parameter and the fourth safety parameter with preset thresholds respectively, and identifying the similarity of the pictures.
In this embodiment, if the third security parameter
If the picture similarity of the first data provider A is smaller than a preset threshold delta, the picture similarity of the first data provider A is high and exceeds a normal range, and infringement is involved, so that certain adjustment is needed; otherwise, the picture similarity of the first data provider a is low.
If the fourth security parameter is
If the picture similarity of the second data provider B is smaller than the preset threshold delta, the picture similarity of the second data provider B is high and exceeds a normal range, and infringement is involved, so that certain adjustment is needed; otherwise, the picture similarity of the second data provider B is low.
The embodiment not only calculates the inner product of the two direction vectors, but also is applied to a realistic scene, so that the safe calculation of the picture similarity is realized, the vector similarity can be calculated more accurately, the application value is higher, and the calculation result is more convincing.
Assume that a computing server
Wanting to fool the first data provider a and the second data provider B, forging the local calculation result +.>
And->
Obviously, if random forgery ++>
And->
The verification of S103 cannot be passed. If want to be pseudoAs a result, and through verification, only one method is to scale up the elements in the vector in the same proportion while ensuring that the elements in the position k are unchanged, but since the position k is kept secret, only the first data provider a and the second data provider B know, so that the calculation server cannot forge the calculation result and pass verification.
According to the embodiment of the invention, the two parties involved not only need to carry out privacy protection on the self parameters, but also need to carry out verifiable calculation on the calculation result, and only when verification is successful, the two parties can prove that the two parties send real data on the premise of not exposing privacy, so that the result calculation result of the picture similarity is effective, and the verification on the correctness of the calculation result is also realized. This obviously improves the security of the solution, i.e. satisfies the condition that n-1 computing servers are malicious.
According to the embodiment, the safe two-party vector Euclidean distance calculation is realized under the malicious model, the two parties respectively perform verifiable calculation on the calculation result, and as long as one party transmits false data, the two parties cannot obtain the correct result, so that the method has more practicability in a real scene.
It should be noted that, for simplicity of description, the foregoing method embodiments are all described as a series of acts, but it should be understood by those skilled in the art that the present invention is not limited by the order of acts described, as some steps may be performed in other orders or concurrently in accordance with the present invention. Further, those skilled in the art will also appreciate that the embodiments described in the specification are alternative embodiments, and that the acts and modules referred to are not necessarily required for the present invention.
The above description of the method embodiments further describes the solution of the present invention by means of device embodiments.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the described modules may refer to corresponding procedures in the foregoing method embodiments, which are not described herein again.
In the technical scheme of the invention, the acquisition, storage, application and the like of the related user personal information all conform to the regulations of related laws and regulations, and the public sequence is not violated.
According to the embodiment of the invention, the invention further provides electronic equipment.
Fig. 2 shows a schematic block diagram of an electronic device 200 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
The device 200 comprises a computing unit 201 that may perform various suitable actions and processes in accordance with computer programs stored in a Read Only Memory (ROM) 202 or loaded from a storage unit 208 into a Random Access Memory (RAM) 203. In the RAM 203, various programs and data required for the operation of the device 200 can also be stored. The computing unit 201, ROM 202, and RAM 203 are connected to each other through a bus 204. An input/output (I/O) interface 205 is also connected to bus 204.
Various components in device 200 are connected to I/O interface 205, including: an input unit 206 such as a keyboard, a mouse, etc.; an output unit 207 such as various types of displays, speakers, and the like; a storage unit 208 such as a magnetic disk, an optical disk, or the like; and a communication unit 209 such as a network card, modem, wireless communication transceiver, etc. The communication unit 209 allows the device 200 to exchange information/data with other devices through a computer network such as the internet and/or various telecommunication networks.
The computing unit 201 may be a variety of general and/or special purpose processing components with processing and computing capabilities. Some examples of computing unit 201 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 201 performs the respective methods and processes described above, for example, the methods S101 to S104. For example, in some embodiments, methods S101-S104 may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as storage unit 208. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 200 via the ROM 202 and/or the communication unit 209. When the computer program is loaded into RAM 203 and executed by computing unit 201, one or more steps of methods S101-S104 described above may be performed. Alternatively, in other embodiments, the computing unit 201 may be configured to perform the methods S101-S104 in any other suitable way (e.g. by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for carrying out methods of the present invention may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus such that the program code, when executed by the processor or controller, causes the functions/operations specified in the flowchart and/or block diagram to be implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and pointing device (e.g., a mouse or trackball) by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), and the internet.
The computer system may include a client and a server. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a cloud server, a server of a distributed system, or a server incorporating a blockchain.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (10)
1. The verifiable picture similarity recognition method based on secret sharing is characterized by comprising the following steps of:
the method comprises the steps that a first data provider generates three random numbers, and the first data provider and a second data provider expand picture vectors according to the three random numbers; slicing the expanded picture vectors of the first data provider and the second data provider, and sending corresponding slices to corresponding computing servers;
the corresponding calculation server calculates a security parameter pair, sends the security parameter pair to a designated calculation server, calculates a first security parameter and a second security parameter by the designated calculation server, sends the first security parameter to the first data provider, and sends the second security parameter to the second data provider;
the first data provider calculates a third security parameter according to the first security parameter, and the second data provider calculates a fourth security parameter according to the second security parameter;
and comparing the third safety parameter and the fourth safety parameter with preset thresholds respectively, and identifying the similarity of the pictures.
2. The method of claim 1, wherein the first data provider performs picture vector expansion based on the three random numbers, comprising:
the first data provider provides a first picture vector, and inserts a first random number into a second random number position in the first picture vector to obtain an expanded first picture vector;
multiplying a third random number by other components except the first random number in the expanded first picture vector to obtain an expanded second picture vector;
and sending the first random number, the second random number and the third random number to a second data provider.
3. The method of claim 2, wherein the second data provider performs picture vector expansion based on the three random numbers, comprising:
the second data provider provides a third picture vector, and inserts the first random number into a second random number position in the third picture vector to obtain an expanded third picture vector; multiplying the third random number with other components except the first random number in the expanded third picture vector to obtain an expanded fourth picture vector.
4. A method according to claim 3, wherein the slicing the extended picture vector of the first and second data providers comprises:
fragmenting the first picture vector expanded by the first data provider to obtain:
fragmenting the second picture vector expanded by the first data provider to obtain:
in the process, 
an i-th slice value of the first picture vector extended for the first data provider,>
is a first random number, ">
Is->
The kth item of (a); />
An ith slice value of a second picture vector extended for said first data provider, ->
Is->
The kth item of (a); k is a second random number; />
Is a third random number;
and slicing the third picture vector expanded by the second data provider to obtain:
and slicing the fourth picture vector expanded by the second data provider to obtain:
wherein, 
an ith slice value of the third picture vector extended for the second data provider,/-j>
Is that
The kth item of (a); />
An i-th slice value of a fourth picture vector extended for the second data provider,/>
Is->
The kth item of (a).
5. The method of claim 4, wherein the pair of security parameters comprises:
wherein (1)>
And->
Respectively setting the security parameters in the security parameter pair, wherein i is the current computing server identifier; n is the dimension of the security parameter; />
Is a component in the first picture vector; />
Is a component in the third picture vector; />
Is a security parameter->
Is a first item of (a); />
Is a security parameter->
A second item of (a); />
Is a first random number, ">
Is a security parameter->
K is a second random number; />
Is a security parameter->
The kth item of (a).
6. The method of claim 5, wherein the first security parameter is:
the second security parameters are:
wherein (1)>
Is a first security parameter; />
Is a second security parameter; />
The parts are slices; />
And w is the counter number.
7. The method of claim 6, wherein the first data provider calculating a third security parameter from the first security parameter comprises:
the second data provider calculates a fourth security parameter according to the second security parameter, including: />
Wherein (1)>
Is a third security parameter; />
Is a fourth security parameter.
8. The method according to claim 1, wherein comparing the third security parameter and the fourth security parameter with preset thresholds, respectively, identifies the similarity of the pictures, and includes:
if the third security parameter is smaller than a preset threshold, the picture similarity of the first data provider is high; otherwise, the picture similarity of the first data provider is low;
if the fourth security parameter is smaller than a preset threshold, the picture similarity of the second data provider is high; otherwise, the picture similarity of the second data provider is low.
9. The method according to claim 1 or 8, further comprising:
judging
If so, verifying the third safety parameter and the fourth safety parameter, and taking the verified third safety parameter and fourth safety parameter as the safety parameters compared with a preset threshold; otherwise, the verification is not passed.
10. An electronic device comprising at least one processor; and
a memory communicatively coupled to the at least one processor; it is characterized in that the method comprises the steps of,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any one of claims 1-9.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20190212986A1 (en) * | 2016-08-18 | 2019-07-11 | Nec Corporation | Secure computation system, secure computation method, secure computation apparatus, distribution information generation apparatus, and methods and programs therefor |
| US20210028926A1 (en) * | 2018-02-20 | 2021-01-28 | Nippon Telegraph And Telephone Corporation | Secure computation device, secure computation authentication system, secure computation method, and program |
| WO2021083179A1 (en) * | 2019-10-30 | 2021-05-06 | 阿里巴巴集团控股有限公司 | Secure multi-party computing method, apparatus, system, and storage medium |
| CN114584294A (en) * | 2022-02-28 | 2022-06-03 | 淘宝（中国）软件有限公司 | Method and device for careless scattered arrangement |
-
2023
- 2023-05-15 CN CN202310538145.6A patent/CN116363395B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20190212986A1 (en) * | 2016-08-18 | 2019-07-11 | Nec Corporation | Secure computation system, secure computation method, secure computation apparatus, distribution information generation apparatus, and methods and programs therefor |
| US20210028926A1 (en) * | 2018-02-20 | 2021-01-28 | Nippon Telegraph And Telephone Corporation | Secure computation device, secure computation authentication system, secure computation method, and program |
| WO2021083179A1 (en) * | 2019-10-30 | 2021-05-06 | 阿里巴巴集团控股有限公司 | Secure multi-party computing method, apparatus, system, and storage medium |
| CN114584294A (en) * | 2022-02-28 | 2022-06-03 | 淘宝（中国）软件有限公司 | Method and device for careless scattered arrangement |
Non-Patent Citations (2)
| Title |
|---|
| LONGDAN TAN ET AL.: "Weighted Secret Image Sharing for a (k; n) Threshold Based on the Chinese Remainder Theorem", 《IEEE ACCESS》, vol. 7 * |
| 陈亚丽;: "基于随机数的图像信息秘密分享技术", 现代电子技术, no. 11 * |
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