Fuzzy privacy set intersection method and system, medium and electronic equipment

Abstract

The invention provides a fuzzy privacy set intersection method and system, a medium and electronic equipment, and the method comprises the steps: carrying out the encryption processing of an initial data set X through a homomorphic encryption algorithm, obtaining first data, and obtaining a processed data structure D through a defined space Hash algorithm; the receiver performs homomorphic addition or scalar multiplication operation processing on the first data by using own data Y, and performs confusion processing by using a random number R to obtain second data; after the sender carries out Augment algorithm processing on the random number, a Hash algorithm is carried out to obtain a corresponding Hash value, and the Hash value is recorded as third data to be sent to the sender; and the sender decrypts the second data, processes the second data through an Augment algorithm, executes a PSI protocol with the receiver to obtain fourth data, and compares the third data with the fourth data to obtain intersection data. According to the method, an addition homomorphic technology is adopted for a novel application scene of intersection of the fuzzy privacy set, and compared with a full homomorphic technology in the prior art, the time consumption is less, and the communication is small.

Description

Technical Field

[0001] The present invention relates to the field of privacy computing technology, and in particular, to a method, system, medium and electronic device for finding the intersection of fuzzy privacy sets. In particular, it relates to a method, system, medium and electronic device for finding the intersection of fuzzy privacy sets in a Euclidean space under a two-party scenario. Background Art

[0002] As users pay more and more attention to data privacy and demand for service convenience, the Private Set Intersection (PSI) technology has limitations in new application scenarios such as private taxi-hailing and biometrics, while the Fuzzy Private Set Intersection (FPSI) technology can be applied to such scenarios. FPSI means that two parties can calculate the fuzzy intersection of their data sets (different from the traditional intersection) without exposing any information other than the intersection. Users or enterprises do not need to worry about their private data being leaked during the FPSI process.

[0003] The existing FPSI methods have problems such as high communication cost, long time consumption, and limited usage scenarios.

[0004] Patent document CN115694777A discloses a privacy set intersection method, device, and electronic device based on homomorphic encryption, which belongs to the field of computer technology. The method includes: in response to receiving an instruction to obtain data intersection for a first participant's data, using an inadvertent pseudo-random function to obtain a random number for each element in the first participant's data; homomorphically encrypting the random number of each element in the first participant's data to obtain an encrypted random number for each element in the first participant's data, and sending the encrypted random number to a second participant; receiving the processing result of the second participant on the encrypted random number and the random number of each element in the second participant's data, and homomorphically decrypting the processing result; determining the data intersection of the first participant's data and the second participant's data based on the processing result after homomorphic decryption, and outputting the data intersection.

[0005] However, the patent document CN115694777A adopts fully homomorphic encryption technology, which will bring high computing and communication overhead. In the scenario of exponential expansion of data sets caused by the intersection problem of fuzzy privacy sets, the calculation time is slow, the communication volume is large, and there is high latency in low-communication networks such as wide area networks. In addition, since the application problem scenario is the intersection of fuzzy privacy sets, which is different from the intersection of privacy sets, it requires that the intersection is allowed to have a certain error. Therefore, this patent document will cause the problem of exponential expansion of data sets, which will eventually lead to exponential growth in communication volume and computing in practical applications. Summary of the invention

[0006] In view of the defects in the prior art, the purpose of the present invention is to provide a fuzzy privacy set intersection method, system, medium and electronic device.

[0007] A fuzzy privacy set intersection method provided by the present invention includes:

[0008] Step S1: When the sender is offline, the initial data set X is encrypted by a predefined homomorphic encryption algorithm to obtain Enc(X), which is recorded as the first data, and the processed data structure D is obtained by a defined spatial-hashing algorithm;

[0009] Step S2: When both the receiver and the sender are online, the receiver obtains the data structure D and decodes it to obtain the first data;

[0010] Step S3: the receiving party performs homomorphic addition or scalar multiplication processing on the first data with its own data Y, and performs obfuscation processing with a random number R to obtain second data, and sends the second data to the sending party;

[0011] Step S4: sending the second data to the sender, and performing a predefined Augment algorithm on the random number R, and then performing a hash algorithm to obtain a corresponding hash value, which is recorded as the third data and sent to the sender;

[0012] Step S5: The sender decrypts the second data, performs a predefined Augment algorithm on the second data, executes the PSI protocol with the receiver, obtains the fourth data, compares the third data with the fourth data, and obtains the intersection data.

[0013] Preferably, the homomorphic encryption algorithm includes an EC-elgamal algorithm that satisfies the additive homomorphic property.

[0014] Preferably, the step S1 comprises:

[0015] Step S1.1: Generate public and private keys (pk, sk) according to the homomorphic encryption algorithm, and send the public key pk to the recipient;

[0016] Step S1.2: The sender uses the public key pk to homomorphically encrypt its own initial data set X and each dimension of the initial data set X to obtain the corresponding encrypted data Enc(x n );

[0017] Step S1.3: Obtain the corresponding spatial value Spatial(x) by using the spatial hash algorithm on the encrypted data n );

[0018] Step S1.4: Use the initial data set X to construct the following key-value pairs for encryption and generate a data structure D in the following form:

[0019] {(Spatial(x1),Enc(x1)),(Spatial(x n ),Enc(x n ))}.

[0020] Preferably, by entering the corresponding key i Decode the value and get the corresponding value i On the contrary, if a value not in the data structure D is input, a random number is obtained;

[0021] The random number R is obtained by randomly running a random function within the data range.

[0022] A fuzzy privacy set intersection system provided by the present invention includes:

[0023] Module M1: When the sender is offline, the initial data set X is encrypted by a predefined homomorphic encryption algorithm to obtain Enc(X), which is recorded as the first data, and the processed data structure D is obtained by a defined spatial-hashing algorithm;

[0024] Module M2: When both the receiver and the sender are online, the receiver obtains the data structure D and decodes it to obtain the first data;

[0025] Module M3: The receiver performs homomorphic addition or scalar multiplication processing on the first data with its own data Y, and performs obfuscation processing with a random number R to obtain second data, and sends the second data to the sender;

[0026] Module M4: sending the second data to the sender, and performing a predefined Augment algorithm on the random number R, and then performing a hash algorithm to obtain a corresponding hash value, which is recorded as the third data and sent to the sender;

[0027] Module M5: The sender decrypts the second data, performs a predefined Augment algorithm on the second data, executes a PSI protocol with the receiver to obtain fourth data, and compares the third data with the fourth data to obtain intersection data.

[0028] Preferably, the homomorphic encryption algorithm includes an EC-elgamal algorithm that satisfies the additive homomorphic property.

[0029] Preferably, the module M1 comprises:

[0030] Module M1.1: Generate public and private keys (pk, sk) according to the homomorphic encryption algorithm, and send the public key pk to the recipient;

[0031] Module M1.2: The sender uses the public key pk to homomorphically encrypt its own initial data set X and each dimension of the initial data set X to obtain the corresponding encrypted data Enc(x n );

[0032] Module M1.3: Obtain the corresponding spatial value Spatial(x) by using the spatial hash algorithm on the encrypted data n );

[0033] Module M1.4: Use the initial data set X to construct the following key-value pairs for encryption and generate a data structure D in the following form:

[0034] {(Spatial(x1),Enc(x1)),(Spatial(x n ),Enc(x n ))}.

[0035] Preferably, by entering the corresponding key i Decode the value and get the corresponding value i On the contrary, if a value not in the data structure D is input, a random number is obtained;

[0036] The random number R is obtained by randomly running a random function within the data range.

[0037] According to a computer-readable storage medium storing a computer program provided by the present invention, when the computer program is executed by a processor, the steps of the fuzzy privacy set intersection method are implemented.

[0038] An electronic device provided according to the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, the steps of the fuzzy privacy set intersection method are implemented.

[0039] Compared with the prior art, the present invention has the following beneficial effects:

[0040] 1. The present invention adopts additive homomorphic technology for the new application scenario of intersection of fuzzy privacy sets, which is less time-consuming and has less communication than the full homomorphic technology in the prior art.

[0041] 2. The present invention adopts a comparison protocol, which is different from the square-level brute force enumeration of fully homomorphic encryption. It only needs to perform logarithmic comparison, which further reduces the calculation and communication overhead, and does not require exponential expansion of the data set, greatly improving the performance.

[0042] 3. The present invention adopts a special combing method, specifically the space hashing technology can effectively avoid the problem of exponential increase in communication volume, making it logarithmically related, and the amount of calculation will also decrease as the amount of communication decreases, so it has the advantages of low communication volume and low calculation amount. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments made with reference to the following drawings:

[0044] Figure 1 It is a schematic diagram of the working method of the present invention;

[0045] Figure 2 Schematic diagram of a binary tree of an Augment algorithm embodiment of the present invention. DETAILED DESCRIPTION

[0046] The present invention is described in detail below in conjunction with specific embodiments. The following embodiments will help those skilled in the art to further understand the present invention, but are not intended to limit the present invention in any form. It should be noted that, for those of ordinary skill in the art, several changes and improvements can also be made without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0047] The application scenario of the present invention includes a Euclidean space in a two-party scenario (receiver and sender), and aims to solve the problem of outputting data whose Euclidean distance is less than or equal to a threshold distance (satisfying the fuzzy intersection definition) from two-party data.

[0048] According to a fuzzy privacy set intersection method provided by the present invention, Figure 1 As shown, including:

[0049] Step S1: When the sender is offline, the initial data set X is encrypted by a predefined homomorphic encryption algorithm to obtain Enc(X), which is recorded as the first data, and the processed data structure D is obtained by a defined spatial-hashing algorithm. The homomorphic encryption algorithm includes the EC-elgamal algorithm that satisfies the additive homomorphic property. The spatial-hashing algorithm refers to a method of hashing data in Euclidean space, with {(key1, value1), ..., (key n ,value n)} form a matrix structure D. Specifically, each coordinate of the initial data set X is divided by twice the threshold distance value to determine the grid number, that is, each coordinate is multiplied by the sum of different coefficients, and then converted into a numerical value, namely key, after the hash algorithm is processed. Then, three different hash functions h1, h2, and h3 are performed on each key to obtain h1(key), h2(key), and h3(key). A matrix M with element values ​​​​of 0 or 1 is constructed, so that the values ​​of the h1(key)th row, h2(key) row, and h3(key) row corresponding to M are XORed and the corresponding value value. After each key is processed as above, the other rows of the remaining matrix M are randomly selected. The final matrix M is D, and the corresponding key can be entered afterwards. i Decode and get the corresponding value i On the contrary, if the input is not in the key set D, a random number is obtained. The step S1 comprises:

[0050] Step S1.1: Generate public and private keys (pk, sk) according to the homomorphic encryption algorithm, and send the public key pk to the recipient.

[0051] Step S1.2: The sender uses the public key pk to homomorphically encrypt its own initial data set X and each dimension of the initial data set X to obtain the corresponding encrypted data Enc(x n ).

[0052] Step S1.3: Obtain the corresponding spatial value Spatial(x) by using the spatial hash algorithm on the encrypted data n ).

[0053] Step S1.4: Use the initial data set X to construct the following key-value pairs for encryption and generate a data structure D in the following form:

[0054] {(Spatial(x1),Enc(x1)),(Spatial(x n ),Enc(x n ))}.

[0055] Step S2: When both the receiver and the sender are online, the receiver obtains the data structure D and obtains the corresponding spatial value Spatial(y) according to its own data Y through the spatial hash algorithm. n), using it as key' to decode and obtain the first data. The decoding includes performing operations on the key' held by itself, performing operations on three different hash functions h1, h2, and h3 to obtain h1(key'), h2(key'), and h3(key'), and then obtaining the value of the corresponding row in the data structure D, performing XOR and final decoding to obtain the corresponding value.

[0056] Step S3: The receiver performs homomorphic addition or scalar multiplication on the first data with its own data Y, and obtains second data after obfuscation with a random number R, and sends the second data to the sender. The random number R is obtained by randomly running a random function within the data range.

[0057] Step S4: Send the second data to the sender, and perform a predefined Augment algorithm on the random number R, and then perform a hash algorithm to obtain the corresponding hash value, which is recorded as the third data and sent to the sender. The Augment algorithm refers to an algorithm for processing whether data A and B satisfy the condition of 0≤|AB|≤γ. After performing the Augment algorithm on A and B respectively, the expanded Aug(A) and Aug(B) sets can be obtained respectively, which ensures that if A and B satisfy 0≤|AB|≤γ, there is an intersection element between the sets Aug(A) and Aug(B), otherwise it is not satisfied. The specific steps include constructing a complete binary tree for data A and B according to their corresponding 01 bit strings, with 0 as the left node and 1 as the right node. Finally, A and B will fall on the leaf nodes of the complete binary tree respectively. For data A, its corresponding 1+logγ parent nodes and itself are selected in turn as the data set after the Augment algorithm. For data B, the leaf nodes in [B-γ, B+γ] are found in turn, and the root nodes of the corresponding child complete binary trees are found in turn as the data set after the Augment algorithm. Figure 2 The specific description is as follows:

[0058] When B is 26 and γ is 5, find a complete binary tree with it as the root node from the complete binary tree, so that the child nodes of the complete binary tree are in [B-γ, B+γ]. In this case, the yellow and green nodes are the roots of the found child complete binary tree, and their child nodes are all nodes in [B-γ, B+γ]. A is 25, and its corresponding 1+logγ parent nodes and predecessor nodes are the blue nodes and green nodes in the figure as the data set after the Augment algorithm. Finally, if the condition 0≤|AB|≤γ is satisfied, the two data sets have an intersection.

[0059] Step S5: The sender decrypts the second data, performs a predefined Augment algorithm on the second data, executes the PSI protocol with the receiver, obtains the fourth data, compares the third data with the fourth data, and obtains the intersection data.

[0060] Furthermore, combined with Figure 1 A method for finding the intersection of fuzzy privacy sets in a Euclidean space under a two-party scenario of the present invention is specifically described as follows:

[0061] The present invention includes two members, namely, a sender and a receiver. The sender and the receiver each hold a data set, which are denoted as X and Y, respectively, and X = {x1, ..., x n},Y={y1,...,y n}, where data Indicates that its data is an element of dimension l. The threshold distance is denoted as d, and the distance in Euclidean space is defined as According to the definition of fuzzy intersection, two data x i and j The Euclidean distance must satisfy the following inequality

[0062] First, the sender selects the additive homomorphic algorithm EC-elgamal and generates a public-private key (pk, sk), and sends the public key pk to the receiver. At the same time, the sender uses the public key pk to store the data X = {x1,...,x n}, where the data Each dimension is homomorphically encrypted as Then, we use spatial-hashing technology to obtain Spatial(x i ), and finally the sender holds X={x1,...,x n}The construction form is {(Spatial(x1),Enc(x1)),(Spatial(x n ),Enc(x n ))}’s key-value pairs are encrypted, and finally a data structure D is generated and sent to the recipient.

[0063] Then, the receiver adds its own data Y = {y1, ..., y n}, get Spatial(y i ) can decode the data structure D obtained from the transmitter to obtain data d. i ) belongs to (Spatial(x1),...,Spatial(x n )), then d corresponds to the encrypted data Enc(x i), otherwise d is considered as a random number.

[0064] Next, the recipient uses the EC-elgamal algorithm to perform homomorphic addition or scalar multiplication on the encrypted data d. Specifically, the result is And for security, a random number r is randomly selected as the confusion number. And send it to the sender. At the same time, the random number r and After subtraction, we get It is denoted as B, and after Augment processing, the hashed data H(r) is sent to the sender.

[0065] Finally, the sender obtains Decrypt using the private key sk to get c, and compare it with Subtract It is denoted as A, and then preprocessed by the Augment algorithm, and the hash algorithm protocol is run with the receiver to obtain H(c), which is finally compared with H(r). If they are the same, they are regarded as intersections.

[0066] The Augment algorithm ensures that, for the case where 0≤|AB|≤γ is satisfied, after A and B are subjected to the Augment algorithm respectively, the data of the two have an intersection. The Augment algorithm can be used to determine whether the Euclidean distance between two data is within the threshold.

[0067] After specific practical experiments, the above method is run on data of different dimensions with a size of 512 for both data sets, under simulated local area network (fast) and wide area network (slow), and the corresponding computing time and communication volume that can be obtained are: The computing time and communication volume of the present invention are improved compared with the existing methods in high-dimensional data above 3 dimensions. Although the computing time overhead is insufficient compared with the existing methods in 2-dimensional data, the communication volume is significantly reduced, as shown in Table 1:

[0068] Table 1

[0069]

[0070] In the experimental table, BP24 refers to the protocol method in Fuzzy Private Set Intersection with LargeHyperballs, and PSI refers to the protocol method in which the traditional PSI is applied to this application scenario. In summary, the fuzzy private set intersection method proposed in the present invention has the advantages of low time consumption and low communication.

[0071] The present invention also provides a fuzzy privacy set intersection system, which can be implemented by executing the process steps of the fuzzy privacy set intersection method, that is, those skilled in the art can understand the fuzzy privacy set intersection method as a preferred implementation of the fuzzy privacy set intersection system.

[0072] A fuzzy privacy set intersection system provided by the present invention includes:

[0073] Module M1: When the sender is offline, the initial data set X is encrypted by a predefined homomorphic encryption algorithm to obtain Enc(X), which is recorded as the first data, and the processed data structure D is obtained by a defined spatial-hashing algorithm. The homomorphic encryption algorithm includes the EC-elgamal algorithm that satisfies the additive homomorphic property. The module M1 includes:

[0074] Module M1.1: Generate public and private keys (pk, sk) according to the homomorphic encryption algorithm, and send the public key pk to the recipient.

[0075] Module M1.2: The sender uses the public key pk to homomorphically encrypt its own initial data set X and each dimension of the initial data set X to obtain the corresponding encrypted data Enc(x n ).

[0076] Module M1.3: Obtain the corresponding spatial value Spatial(x) by using the spatial hash algorithm on the encrypted data n ).

[0077] Module M1.4: Use the initial data set X to construct the following key-value pairs for encryption and generate a data structure D in the following form:

[0078] {(Spatial(x1),Enc(x1)),(Spatial(x n ),Enc(x n ))}.

[0079] Module M2: When the receiver and the sender are both online, the receiver obtains the data structure D and decodes it to obtain the first data.

[0080] Module M3: The receiver performs homomorphic addition or scalar multiplication on the first data using its own data Y, performs obfuscation processing using a random number R, obtains second data, and sends the second data to the sender.

[0081] Module M4: Send the second data to the sender, and perform a predefined Augment algorithm on the random number R, and then perform a hash algorithm to obtain a corresponding hash value, which is recorded as the third data and sent to the sender. i Decode the value and get the corresponding value i On the contrary, if a value not in the data structure D is input, a random number is obtained. The random number R is obtained by randomly running a random function within the data range.

[0082] Module M5: The sender decrypts the second data, performs a predefined Augment algorithm on the second data, executes a PSI protocol with the receiver to obtain fourth data, and compares the third data with the fourth data to obtain intersection data.

[0083] According to a computer-readable storage medium storing a computer program provided by the present invention, when the computer program is executed by a processor, the steps of the fuzzy privacy set intersection method are implemented.

[0084] An electronic device provided according to the present invention includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, the steps of the fuzzy privacy set intersection method are implemented.

[0085] Those skilled in the art know that, in addition to realizing the system and its various devices, modules, and units provided by the present invention in a purely computer-readable program code, it is entirely possible to realize the same functions in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers by logically programming the method steps. Therefore, the system and its various devices, modules, and units provided by the present invention can be considered as a hardware component, and the devices, modules, and units included therein for realizing various functions can also be regarded as structures within the hardware component; the devices, modules, and units for realizing various functions can also be regarded as both software modules for realizing the method and structures within the hardware component.

[0086] The above describes the specific embodiments of the present invention. It should be understood that the present invention is not limited to the above specific embodiments, and those skilled in the art can make various changes or modifications within the scope of the claims, which does not affect the essence of the present invention. In the absence of conflict, the embodiments of the present application and the features in the embodiments can be combined with each other arbitrarily.

Claims

1. A fuzzy privacy set intersection method, characterized in that: include: Step S1: When the sender is offline, the initial data set X is encrypted by a predefined homomorphic encryption algorithm to obtain Enc(X), which is recorded as the first data, and the processed data structure D is obtained by a defined spatial-hashing algorithm; Step S2: When both the receiver and the sender are online, the receiver obtains the data structure D and decodes it to obtain the first data; Step S3: the receiving party performs homomorphic addition or scalar multiplication processing on the first data with its own data Y, and performs obfuscation processing with a random number R to obtain second data, and sends the second data to the sending party; Step S4: sending the second data to the sender, and performing a predefined Augment algorithm on the random number R, and then performing a hash algorithm to obtain a corresponding hash value, which is recorded as the third data and sent to the sender; Step S5: The sender decrypts the second data, performs a predefined Augment algorithm on the second data, executes the PSI protocol with the receiver, obtains the fourth data, compares the third data with the fourth data, and obtains the intersection data.

2. The fuzzy privacy set intersection method according to claim 1, characterized in that: The homomorphic encryption algorithm includes the EC-elgamal algorithm that satisfies the additive homomorphic property.

3. The fuzzy privacy set intersection method according to claim 1, characterized in that: The step S1 comprises: Step S1.1: Generate public and private keys (pk, sk) according to the homomorphic encryption algorithm, and send the public key pk to the recipient; Step S1.2: The sender uses the public key pk to homomorphically encrypt its own initial data set X and each dimension of the initial data set X to obtain the corresponding encrypted data Enc(x n ); Step S1.3: Obtain the corresponding spatial value Spatial(x) by using the spatial hash algorithm on the encrypted data n ); Step S1.4: Use the initial data set X to construct the following key-value pairs for encryption and generate a data structure D in the following form: {(Spatial(x1),Enc(x1)),(Spatial(x n ),Enc(x n ))}。 4. The fuzzy privacy set intersection method according to claim 1, characterized in that: By entering the corresponding key i Decode the value and get the corresponding value i On the contrary, if a value not in the data structure D is input, a random number is obtained; The random number R is obtained by randomly running a random function within the data range.

5. A fuzzy privacy set intersection system, characterized in that: include: Module M1: When the sender is offline, the initial data set X is encrypted by a predefined homomorphic encryption algorithm to obtain Enc(X), which is recorded as the first data, and the processed data structure D is obtained by a defined spatial-hashing algorithm; Module M2: When both the receiver and the sender are online, the receiver obtains the data structure D and decodes it to obtain the first data; Module M3: The receiver performs homomorphic addition or scalar multiplication processing on the first data with its own data Y, and performs obfuscation processing with a random number R to obtain second data, and sends the second data to the sender; Module M4: sending the second data to the sender, and performing a predefined Augment algorithm on the random number R, and then performing a hash algorithm to obtain a corresponding hash value, which is recorded as the third data and sent to the sender; Module M5: The sender decrypts the second data, performs a predefined Augment algorithm on the second data, executes a PSI protocol with the receiver to obtain fourth data, and compares the third data with the fourth data to obtain intersection data.

6. The fuzzy privacy set intersection system according to claim 5, characterized in that: The homomorphic encryption algorithm includes the EC-elgamal algorithm that satisfies the additive homomorphic property.

7. The fuzzy privacy set intersection system according to claim 5, characterized in that: The module M1 comprises: Module M1.1: Generate public and private keys (pk, sk) according to the homomorphic encryption algorithm, and send the public key pk to the recipient; Module M1.2: The sender uses the public key pk to homomorphically encrypt its own initial data set X and each dimension of the initial data set X to obtain the corresponding encrypted data Enc(x n ); Module M1.3: Obtain the corresponding spatial value Spatial(x) by using the spatial hash algorithm on the encrypted data n ); Module M1.4: Use the initial data set X to construct the following key-value pairs for encryption and generate a data structure D in the following form: {(Spatial(x1),Enc(x1)),(Spatial(x n ),Enc(x n ))}。 8. The fuzzy privacy set intersection system according to claim 5, characterized in that: By entering the corresponding key i Decode the value and get the corresponding value i On the contrary, if a value not in the data structure D is input, a random number is obtained; The random number R is obtained by randomly running a random function within the data range.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the fuzzy privacy set intersection method described in any one of claims 1 to 4 are implemented.

10. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein: When the computer program is executed by a processor, the steps of the fuzzy privacy set intersection method described in any one of claims 1 to 4 are implemented.

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