A biometric-based zero-knowledge identity recognition method and system

Abstract

The application discloses a kind of zero-knowledge identity recognition method and system based on biological characteristics, by the biological characteristic data of the example collected and the biological characteristic data of user are preprocessed, and using elliptic curve and bilinear mapping method, according to the biological characteristic corresponding to example, construct zero-knowledge proof system, and then using zero-knowledge proof system, according to the biological characteristic corresponding to user, construct common data set CRS, and according to common data set CRS, calculate proof data and verification data, obtain proof data set and verification data set, and compare proof data set and verification data set, obtain identity recognition result;The application utilizes elliptic curve and bilinear mapping method, and constructs zero-knowledge identity recognition system based on biological characteristics, reduces the storage overhead of biological characteristic data, while ensuring the uniqueness of zero-knowledge proof input, solves the technical problems of biological characteristic data leakage, data abuse, information forgery and other risks.

Description

Technical Field

[0001] This invention relates to the field of zero-knowledge identity recognition technology, and more specifically to a zero-knowledge identity recognition method based on biometrics. Background Technology

[0002] Biometrics refers to the unique and stable characteristics inherent to each individual. Biometric identification technology can solve the problems of password memory and key storage in identity authentication. A biometric system samples biometric features, extracts their characteristics, converts them into digital codes, further assembles these codes into feature templates, and stores the feature templates on the server.

[0003] Zero-knowledge proofs refer to proofs that can convince a verifier that a statement is true without the verifier providing any valid information. These include interactive and non-interactive zero-knowledge proofs; non-interactive zero-knowledge proofs effectively prevent the possibility of cheating. A zero-knowledge proof system is a system that takes a secret reference string as input and verifies it through a specific procedure.

[0004] However, if the biometric template in identity authentication or the reference string in zero-knowledge proof is leaked, it will cause the entire authentication system to crash, requiring the zero-knowledge proof system to be reset. There is a lack of existing technologies that can reduce the storage overhead of biometric data on the server side while ensuring the uniqueness of the input of zero-knowledge proof. Summary of the Invention

[0005] To address the aforementioned shortcomings in existing technologies, this invention provides a zero-knowledge identity recognition method and system based on biometrics. By utilizing elliptic curve and bilinear mapping methods, a zero-knowledge proof system is constructed based on biometrics and directly authenticated with a public dataset. This reduces the storage overhead of biometric data while ensuring the uniqueness of the zero-knowledge proof input.

[0006] To achieve the above-mentioned objectives, the technical solution adopted by this invention is as follows:

[0007] On the one hand, a zero-knowledge identity recognition method based on biometrics includes the following steps:

[0008] S1. Collect biometric data from the embodiment and biometric data from the user, and preprocess the collected biometric data to obtain the biometric data corresponding to the embodiment and the biometric data corresponding to the user, respectively.

[0009] S2. Using elliptic curves and bilinear mapping, construct a zero-knowledge proof system based on the biometric features corresponding to the embodiments;

[0010] S3. Construct a common dataset CRS based on the biometric features of users using a zero-knowledge proof system;

[0011] S4. Calculate the proof data and verification data respectively based on the common dataset CRS to obtain the proof dataset and verification dataset, and compare the proof dataset and verification dataset, and use the comparison result as the identity recognition result.

[0012] Preferably, step S1 specifically includes the following sub-steps:

[0013] A1. Collect biometric data from the examples and users, as well as a verification dataset, and perform grayscale processing on each biometric data to obtain processed biometric data.

[0014] A2. Extract the column vectors of the processed biometric data and construct a vector matrix;

[0015] A3. Optimize the vector matrix according to the preset dimensions to obtain the biometric features corresponding to the embodiment and the biometric features corresponding to the user.

[0016] Preferably, the zero-knowledge proof system in step S2 is represented as follows:

[0017] M = B 1n *A nm *X m1 +B 1n '*A n1 ',L=A n1 T +B 1n +B 1n '+(A n1 ') T

[0018] Where M is the polynomial value, X m1 For the privacy value of the witness, A nm B is a vector matrix based on face data. 1n A is a biometric matrix based on eye data. n1 'B' represents the biometric matrix based on facial data under a single authentication process. 1n ' is the biometric matrix based on eye data for a single authentication, and L is the constraint relationship. (.) T For transpose, A n1 Let A be a vector matrix based on face data. nm The standard vector is denoted by *, where * represents the vector multiplication symbol.

[0019] Preferably, step S3 specifically includes:

[0020] Based on the bilinear verification relation and the operational rules of elliptic curves, a zero-knowledge proof system is used to calculate encryption parameters based on the user's biometric features under a pre-defined homomorphic hiding. A common dataset CRS is then constructed based on these calculated encryption parameters. Specifically, the common dataset CRS is as follows:

[0021] E1(1),E1(α),E1(M0),E1(L),E1(M),

[0022] E1(B 1n ),E1(A 1n T +B 1n +B 1n '+(A n1 ') T )

[0023] E2(1),E2(α),E2(M0),E2(L),E2(M),

[0024] E2(X m1 ),E2(A nm *X m1 ),E2(A 1n T +B 1n +B 1n '+(A n1 ') T )

[0025] Where E1(.) and E2(.) are the first operator and the second operator, respectively, and α is a random number.

[0026] Preferably, step S4 includes the following sub-steps:

[0027] B1. Calculate the proof data based on the common dataset CRS, and construct the proof dataset based on the proof data values, where the proof dataset is represented as:

[0028] e(E1(B 1n ),E2(A nm *X m1 )), e(E1(M0),E2(X))+e(E1(B 1n '),E2(A n1 ')), e(E2(A 1n T +B 1n +B 1n '+(A n1 ') T ),E2(α));

[0029] Here, e(.) is the third operator;

[0030] B2. Calculate validation data based on the common dataset CRS, and construct a validation dataset based on the validation data values, where the validation dataset is represented as:

[0031] e(E1(B 1n *A nm ),E2(X m1 )), e(E1(X),E2(M0)), e(E1(1),E2(M)), e(E1(α),E2(L));

[0032] B3. Compare the proof dataset and the verification dataset according to the preset mapping parameters, and determine whether the mapping values ​​between the proof dataset and the verification dataset are equal. If they are equal, the identity recognition of the verification dataset is successful; otherwise, the identity recognition is successful.

[0033] Secondly, a zero-knowledge identity recognition system based on biometrics includes:

[0034] The data preprocessing module is used to collect biometric data of the embodiment and biometric data of the user, and preprocess the collected biometric data to obtain the biometric data corresponding to the embodiment and the biometric data corresponding to the user, respectively.

[0035] A zero-knowledge proof system construction module is used to construct a zero-knowledge proof system based on the biometric features corresponding to the embodiments using elliptic curves and bilinear mapping methods.

[0036] The Common Dataset CRS building module is used to construct a Common Dataset CRS based on the user's corresponding biometric features using a zero-knowledge proof system;

[0037] The identity verification module is used to calculate the proof data and verification data based on the common dataset CRS, obtain the proof dataset and verification dataset, compare the proof dataset and verification dataset, and use the comparison result as the identity recognition result.

[0038] The present invention has the following beneficial effects:

[0039] The invention preprocesses the collected biometric data of the embodiments and the user's biometric data, and uses elliptic curve and bilinear mapping methods to construct a zero-knowledge proof system based on the biometrics corresponding to the embodiments. Then, it uses the zero-knowledge proof system to construct a common dataset CRS based on the user's corresponding biometrics, and calculates proof data and verification data based on the common dataset CRS to obtain the proof dataset and verification dataset. By comparing the proof dataset and verification dataset, the identity recognition result is obtained. This invention utilizes elliptic curve and bilinear mapping methods and constructs a zero-knowledge identity recognition system based on biometrics, reducing the storage overhead of biometric data while ensuring the uniqueness of the zero-knowledge proof input, thus solving the technical problems of biometric data leakage, data abuse, and information forgery risks in existing technologies. Attached Figure Description

[0040] Figure 1 A flowchart illustrating the steps of a zero-knowledge identity recognition method based on biometrics provided by this invention;

[0041] Figure 2 This is a flowchart of a trusted third-party construction verification system in an embodiment of the present invention. Detailed Implementation

[0042] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0043] like Figure 1 As shown, this embodiment of the invention provides a zero-knowledge identity recognition method based on biometrics, including the following steps:

[0044] S1. Collect biometric data from the embodiment and biometric data from the user, and preprocess the collected biometric data to obtain the biometric data corresponding to the embodiment and the biometric data corresponding to the user, respectively.

[0045] like Figure 2 As shown, in this embodiment of the invention, relevant biometric data and verification datasets can be obtained from a server (trusted third party). The biometric data can be: face images, eye images, etc. The obtained images are processed, such as: grayscale conversion, resizing, data standardization, etc.; and the implementation method and corresponding verification relationship adopted by the verification system are determined through the preprocessed data, and the relevant biometric data are hidden and the CRS dataset is made public.

[0046] Preferably, step S1 specifically includes the following sub-steps:

[0047] A1. Collect the biometric data of the embodiment and the user, as well as the verification data set, and perform gray-scale processing on each biometric data to obtain the processed biometric data;

[0048] In the embodiment of the present invention, after performing gray-scale processing on each biometric data, image resetting is performed to obtain the processed biometric data of a fixed size.

[0049] In the embodiment of the present invention, a third party can extract biometric data through a terminal device and select biometric data A and biometric data B. Among them, the face data in the biometric data can be used as A, and the eye data in the biometric data can be used as B.

[0050] A2. Extract the column vectors of each processed biometric data and construct a vector matrix;

[0051] In the embodiment of the present invention, the face data A in the biometric data is converted into a column vector, and its column vector is expressed as: (a 11 , a 12 ,..., a 1n ). The acquisition method is specifically as follows: The face data A after gray-scale processing obtains a matrix, and each row of the matrix is regarded as a vector; similarly, the eye data B can be converted into a vector (b 11 , b 12 ,..., b 1n ); and the data collected m (m≥2) times are all processed in the above manner, and finally a vector matrix A nm based on the face data and a vector matrix B mn based on the eye data are obtained respectively.

[0052] A3. Optimize the vector matrix according to a preset dimension to obtain the biometric features corresponding to the embodiment and the biometric features corresponding to the user respectively.

[0053] In the embodiment of the present invention, taking the face data A in the biometric data corresponding to the embodiment as an example:

[0054] According to the preset dimension, that is, averaging the vector matrix in the same dimension to obtain an average value, and optimizing each element in the vector matrix according to the difference between the value corresponding to the preset dimension and the average value, a biometric matrix based on the face data is obtained, and its biometric matrix based on the face data is expressed as: Among them, A ij is the biometric matrix based on the face data, 0 < i < n, 0 < j < m, n is the number of elements of the feature vector, a ijLet m be the vector matrix element in the i-th row and j-th column, and m be the number of collections, with the value satisfying: m≥2;

[0055] Taking eye data B from the biometric data corresponding to the example as an example:

[0056] The eye data matrix is ​​summed and rounded down along the same dimension to reduce differential data. The biometric matrix based on eye data is represented as follows: Among them, B 1n b is a biometric matrix based on eye data. in Let be the element of the vector matrix in the i-th row and n-th column.

[0057] Similarly, the server obtains the user's biometric data, A' = (a' 11 ,a' 12 ,...,a' 1n ) represents plaintext facial image data, i.e., the user's biometric features based on the facial image, B' = (b' 11 ,b' 12 ,...,b' 1n () represents plaintext data of the eye image, i.e., the user's biometric features based on the eye image.

[0058] S2. Using elliptic curves and bilinear mapping, construct a zero-knowledge proof system based on the biometric features corresponding to the embodiments;

[0059] In this embodiment of the invention, by adding variable privacy factors, the biometric template is prevented from becoming a single target of attack. The proof system based on elliptic curve construction protects the user's privacy data and increases the credibility of the dataset.

[0060] Preferably, the zero-knowledge proof system in step S2 is represented as follows:

[0061] M = B 1n *A nm *X m1 +B 1n '*A n1 ',L=A n1 T +B 1n +B 1n '+(A n1 ') T

[0062] Where M is the polynomial value, X m1 For the privacy value of the witness, A nm B is a vector matrix based on face data. 1n A is a biometric matrix based on eye data. n1 'B' represents the biometric matrix based on facial data under a single authentication process.1n ' is the biometric matrix based on eye data for a single authentication, and L is the constraint relationship. (.) T For transpose, A n1 Let A be a vector matrix based on face data. nm The standard vector is denoted by *, where * represents the vector multiplication symbol.

[0063] In this embodiment of the invention, preprocessed biometric features are segmented to obtain segmented biometric features, wherein and a zero-knowledge proof system is constructed by combining the segmented biometric features using polynomial equations, wherein the biometric feature matrix A based on face data is used. ij As a matrix variable in embodiment M, the biometric matrix B based on eye data 1n The private vector of embodiment M is supplemented or truncated; and the constant term C of the equation is obtained by acquiring face data A and eye data B in a single authentication process. Thus, embodiment M = M0*X + C = A*B*X + C is constructed, where X is the private variable input by the user during the proof, and M0 is the coefficient vector of the polynomial equation.

[0064] In this method, homomorphic hiding can be selected to calculate the parameters. E1 represents the operation of the elliptic curve generator point G1 in the first-order domain, and E2 represents the operation of the elliptic curve generator point G2 in the second-order domain. Both homomorphic hiding E1 and E2 use cyclic groups of prime order. Furthermore, based on the bilinear verification relation and the operational rules of elliptic curves, a mixture of the two operations is used for specific homomorphic hiding operations to calculate the relevant parameters, where each parameter satisfies: M0 = B 1n *A nm C = B 1n '*A n1 ', L = A n1 T +B 1n +C B +C A T C A For a single authentication, the biometric matrix A based on facial data is... n1 The simplified representation of ', C B B is the biometric matrix based on eye data for single authentication. 1n The simplified representation of ', where L is the constraint relation.

[0065] like Figure 2 As shown, the server calculates M0 as a statement, which will then be sent to the validator.

[0066] Based on M0, L, and the constant term C in the equation, the sequence value E(M0), the first mapping value E(C), the second mapping value E(M), and the third mapping value E(L) in the initialization embodiment are obtained respectively to construct a common dataset CRS. In this process, a non-interactive zero-knowledge proof method based on biometrics is used. Due to the uniqueness and stability of biometrics, the proof process is not only safe and reliable, but also reduces the interactive process of identity authentication.

[0067] S3. Construct a common dataset CRS based on the biometric features of users using a zero-knowledge proof system;

[0068] In this embodiment of the invention, when a user applies for identity authentication, the terminal acquires the user's biometric data in real time; and verifies the user's identity on the server, generating a private vector X for them. m1 As a witness, and send the witness information to the user in encryption;

[0069] In this embodiment of the invention, the sequence value E(M0), the first mapping value E(C), the second mapping value E(M), and the third mapping value E(L) in the initialization embodiment can be obtained according to M0, L, and the constant term C of the equation to construct a common dataset CRS. In this process, the non-interactive zero-knowledge proof method based on biometrics not only makes the proof process safe and reliable due to the uniqueness and stability of biometrics, but also reduces the interactive process of identity authentication.

[0070] Preferably, step S3 specifically includes:

[0071] Based on bilinear verification relations and elliptic curve operation rules, a zero-knowledge proof system is used to calculate encryption parameters based on the user's biometric features under a pre-defined homomorphic concealment. A common dataset CRS is then constructed based on these calculated encryption parameters. The common dataset CRS contains data processed by E1 and E2. E1, representing an elliptic curve operation, can be expressed as E1(α) = [α]P1, and E2, representing an elliptic curve operation, can be expressed as E2(α) = [α]P2. Specifically:

[0072] E1(1),E1(α),E1(M0),E1(L),E1(M),

[0073] E1(B 1n ),E1(A 1n T +B 1n +B 1n '+(A n1 ') T )

[0074] E2(1),E2(α),E2(M0),E2(L),E2(M),

[0075] E2(Xm1 ),E2(A nm *X m1 ),E2(A 1n T +B 1n +B 1n '+(A n1 ') T )

[0076] Where E1(.) and E2(.) are the first operator and the second operator, respectively, and α is a random number.

[0077] In this embodiment of the invention, the detailed process of the first operator E1 includes: when the server randomly selects a value, i.e., α is a random value, then E1(α) = [α]G1 is calculated, that is, elliptic curve calculation is performed with α as the coefficient and G1 as the generator; when the value B is selected... 1n When the vector is used, E1(B) is calculated. 1n )=[B 11 G1+…+[B 1n G1, i.e., vector B 1n Each element is a coefficient, and G1 is a generator. Elliptic curve calculations are performed on each element and then summed. The second operator E2 can be obtained in the same way.

[0078] S4. Calculate the proof data and verification data respectively based on the common dataset CRS to obtain the proof dataset and verification dataset, and compare the proof dataset and verification dataset, and use the comparison result as the identity recognition result.

[0079] Preferably, step S4 includes the following sub-steps:

[0080] B1. Calculate the proof data based on the common dataset CRS, and construct the proof dataset based on the proof data values, where the proof dataset is represented as:

[0081] e(E1(B 1n ),E2(A nm *X m1 )), e(E1(M0),

[0082] E2(X))+e(E1(B 1n '),E2(A n1 ')), e(E2(A 1n T +B 1n +B 1n '+(A n1 ') T ),E2(α));

[0083] Here, e(.) is the third operator;

[0084] In this embodiment of the invention, the calculation process of this e(.) can be described as follows: Assume G1, G2, G... r All are cyclic groups of order p, where p is a prime number. g1 is a generator of group G1, and g2 is a generator of group G2. e is a bilinear mapping e: G1 × G2 → G T It satisfies the following properties:

[0085] 1. Bilinearity: For and There is e(g1) a g2 b ) = e(g1, g2) ab ;

[0086] 2. Non-degradability: Make here The unit element representing the G2 group;

[0087] 3. Computability: There exists an efficient pair of algorithms. The value of e(g1, g2) can be calculated.

[0088] B2. Calculate validation data based on the common dataset CRS, and construct a validation dataset based on the validation data values, where the validation dataset is represented as:

[0089] e(E1(B 1n *A nm ),E2(X m1 )), e(E1(X),E2(M0)), e(E1(1),E2(M)), e(E1(α),E2(L));

[0090] B3. Compare the proof dataset and the verification dataset according to the preset mapping parameters, and determine whether the mapping values ​​between the proof dataset and the verification dataset are equal. If they are equal, the identity recognition of the verification dataset is successful; otherwise, the identity recognition is successful.

[0091] In this embodiment of the invention, the comparison process can be represented as follows:

[0092]

[0093] Among them, C A For a single authentication, the biometric matrix A based on facial data is... n1 The simplified representation of ', C B B is the biometric matrix based on eye data for single authentication. 1n The simplified representation of ' is L, which is a constraint relation. It determines whether the mapping values ​​of each row of calculations are equal. If they are equal, the authentication is successful; otherwise, it fails.

[0094] Secondly, embodiments of the present invention provide a zero-knowledge identity recognition system based on biometrics, comprising:

[0095] The data preprocessing module is used to collect biometric data of the embodiments and biometric data of the users, as well as the verification dataset. At the same time, it preprocesses the collected biometric data to obtain the biometric data corresponding to the embodiments and the biometric data corresponding to the users.

[0096] A zero-knowledge proof system construction module is used to construct a zero-knowledge proof system based on the biometric features corresponding to the embodiments using elliptic curves and bilinear mapping methods.

[0097] The Common Dataset CRS building module is used to construct a Common Dataset CRS based on the user's corresponding biometric features using a zero-knowledge proof system;

[0098] The identity verification module is used to calculate the proof data and verification data based on the common dataset CRS, obtain the proof dataset and verification dataset, compare the proof dataset and verification dataset, and use the comparison result as the identity recognition result.

[0099] The present invention provides a zero-knowledge identity recognition system based on biometrics, which includes all the beneficial effects of the above-mentioned zero-knowledge identity recognition method based on biometrics.

[0100] This 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 block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can 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, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0101] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0102] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0103] Specific embodiments have been used to illustrate the principles and implementation methods of this invention. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of this invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this invention. Therefore, the content of this specification should not be construed as a limitation of this invention.

[0104] Those skilled in the art will recognize that the embodiments described herein are intended to help the reader understand the principles of the invention, and should be understood that the scope of protection of the invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific modifications and combinations based on the technical teachings disclosed in this invention without departing from the spirit of the invention, and these modifications and combinations are still within the scope of protection of this invention.

Claims

1. A zero-knowledge identity recognition method based on biometrics, characterized in that, Includes the following steps: S1. Collect biometric data from the embodiment and biometric data from the user, and preprocess the collected biometric data to obtain the biometric data corresponding to the embodiment and the biometric data corresponding to the user, respectively. S2. Using elliptic curves and bilinear mapping, a zero-knowledge proof system is constructed based on the biometric features corresponding to the embodiments. The zero-knowledge proof system is represented as follows: M = B 1n *A nm *X m1 + B 1n ’*A n1 ', L = A n1 T + B 1n + B 1n ’+ （A n1 ’） T in, M For polynomial values, X m1 For the privacy value of the witness, A nm It is a vector matrix based on face data. B 1n A biometric matrix based on eye data. A n1 ’ This is a biometric matrix based on facial data for a single authentication process. B 1n ’ This is a biometric matrix based on eye data for a single authentication process. L For constraint relations L For constraint relations, (.) T For transpose, A n1 A vector matrix based on face data A nm The standard vector is , and * is the vector multiplication symbol; S3. Construct a common dataset CRS based on the user's biometric features using a zero-knowledge proof system; based on the bilinear verification relation and the operational rules of elliptic curves, calculate encryption parameters based on the user's biometric features using a zero-knowledge proof system under a pre-defined homomorphic hiding condition, and construct the common dataset CRS based on the calculated encryption parameters. The common dataset CRS is specifically as follows: E 1 (1), E 1 (α), E 1 (M 0 ), E 1 (L), E 1 (M), E 1 (B 1n ), E 1 (A 1n T + B 1n + B 1n ’+（A n1 ’） T ) E 2 (1), E 2 (α), E 2 (M 0 ), E 2 (L), E 2 (M), E 2 (X m1 ), E 2 (A nm *X m1 ), E 2 (A 1n T + B 1n + B 1n ’+(A n1 ’) T ) in, E 1 (.)、E 2 (.) These are the first operator and the second operator, respectively. α It is a random number; M 0 This is the coefficient vector of the polynomial equation; S4. Calculate the proof data and verification data respectively based on the common dataset CRS to obtain the proof dataset and verification dataset, and compare the proof dataset and verification dataset, and use the comparison result as the identity recognition result.

2. The zero-knowledge identity recognition method based on biometrics according to claim 1, characterized in that, Step S1 specifically includes the following sub-steps: A1. Collect biometric data from the examples and users, as well as a verification dataset, and perform grayscale processing on each biometric data to obtain processed biometric data. A2. Extract the column vectors of the processed biometric data and construct a vector matrix; A3. Optimize the vector matrix according to the preset dimensions to obtain the biometric features corresponding to the embodiment and the biometric features corresponding to the user.

3. The zero-knowledge identity recognition method based on biometrics according to claim 1, characterized in that, Step S4 includes the following sub-steps: B1. Calculate the proof data based on the common dataset CRS, and construct the proof dataset based on the proof data values, where the proof dataset is represented as: e(E 1 (B 1n ), E 2 (A nm *X m1 )) ， e(E 1 (M 0 ), E 2 (X))+e(E 1 (B 1n ’), E 2 (A n1 ’)) ， e(E 2 (A 1n T + B 1n + B 1n ’+(A n1 ’) T ), E 2 (α)) ； in, e(.) For the third operator; X For proof purposes, the user inputs a private variable; B2. Calculate validation data based on the common dataset CRS, and construct a validation dataset based on the validation data values, where the validation dataset is represented as: e(E 1 (B 1n *A nm ), E 2 (X m1 )) ， e(E 1 (X), E 2 (M 0 )) ， e(E 1 (1), E 2 (M)) ， e(E 1 (α), E 2 (L)) ； B3. Compare the proof dataset and the verification dataset according to the preset mapping parameters, and determine whether the mapping values ​​between the proof dataset and the verification dataset are equal. If they are equal, the identity recognition of the verification dataset is successful; otherwise, the identity recognition is successful.

4. A zero-knowledge identity recognition system based on biometrics, implementing the method described in any one of claims 1-3, characterized in that, include: The data preprocessing module is used to collect biometric data of the embodiment and biometric data of the user, and preprocess the collected biometric data to obtain the biometric data corresponding to the embodiment and the biometric data corresponding to the user, respectively. A zero-knowledge proof system construction module is used to construct a zero-knowledge proof system based on the biometric features corresponding to the embodiments using elliptic curves and bilinear mapping methods. The Common Dataset CRS building module is used to construct a Common Dataset CRS based on the user's corresponding biometric features using a zero-knowledge proof system; The identity verification module is used to calculate the proof data and verification data based on the common dataset CRS, obtain the proof dataset and verification dataset, compare the proof dataset and verification dataset, and use the comparison result as the identity recognition result.

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