Anonymous identity authentication method, system and program product based on privacy computing

Abstract

The present invention belongs to the field of information security technology, and specifically discloses an anonymous identity authentication method, system and program product based on privacy computing, which collects the user's biometric information and anonymous authentication request information, calculates the hash value using the login ID and password data to perform hash value verification, and after the hash value verification passes, calculates the vector association parameter between the biometric vector and the verification feature vector to determine the distance threshold, and then performs data conversion processing and character extraction and combination on the desensitized personal information to obtain a combined string, and finally determines whether the user's anonymous identity authentication passes based on the Hamming distance and the distance threshold between the combined string and the verification string, so as to realize efficient anonymous identity authentication based on privacy computing. The present invention can effectively realize user anonymous identity authentication through corresponding anonymous credential calculation and verification, prevent identity fraud and user personal privacy leakage, and ensure the reliability and stability of user identity authentication.

Description

Technical Field

[0001] The present invention belongs to the field of information security technology, and specifically relates to an anonymous identity authentication method, system and program product based on privacy computing. Background Art

[0002] With the rapid development of information technology, information security is of vital importance, and privacy and personal information protection are increasingly valued. Identity authentication, as a way to confirm the legitimacy of the requester, is widely used in various Internet services and digital information management systems. At present, when users perform identity authentication, they generally adopt an identity authentication method based on real-name information, that is, uploading the real identity information of the individual to the authentication background for identity information verification and authentication. This authentication method can neither meet the user's desire not to use too much personal information, nor can it easily lead to the leakage of personal privacy information during the authentication process. Therefore, there is an urgent need for an effective anonymous identity authentication method. Summary of the invention

[0003] The purpose of the present invention is to provide an anonymous identity authentication method, system and program product based on privacy computing to solve the above-mentioned problems existing in the prior art.

[0004] In order to achieve the above object, the present invention adopts the following technical solutions:

[0005] First, an anonymous identity authentication method based on privacy computing is provided, including:

[0006] Collecting the user's biometric information and obtaining anonymous authentication request information submitted by the user, wherein the biometric information includes a biometric vector, and the anonymous authentication request information includes a login ID, password data, and desensitized personal information;

[0007] The initial authentication information is composed of the login ID and password data, and a hash value is calculated for the initial authentication information to obtain a corresponding target hash value;

[0008] Retrieving the corresponding verification hash value from the first verification library according to the password data, and using the verification hash value to perform consistency verification on the target hash value. If the target hash value is inconsistent with the verification hash value, it is determined that the user anonymous identity authentication fails;

[0009] When the target hash value is consistent with the verification hash value, the verification feature vector corresponding to the password data is retrieved from the feature library, and the data mask and verification string corresponding to the password data are retrieved from the second verification library;

[0010] Calculating a vector correlation parameter between the biometric feature vector and the verification feature vector, and determining a corresponding distance threshold according to the vector correlation parameter;

[0011] Perform data conversion processing on the desensitized personal information to obtain the corresponding desensitized character string;

[0012] Using data mask to extract characters from the desensitized character string, and combining the extracted characters to obtain a combined character string;

[0013] Determine the Hamming distance between the combined string and the check string;

[0014] When the Hamming distance is less than the distance threshold, it is determined that the user anonymous identity authentication is passed; otherwise, it is determined that the user anonymous identity authentication is failed.

[0015] In one possible design, the anonymous authentication request information also includes a timestamp. Before using the login ID and password data to form the initial verification information, the method further includes:

[0016] Determine the number of anonymous identity authentication requests made by the corresponding user within a set historical time period based on the timestamp and login ID;

[0017] When the number of anonymous identity authentication requests of the corresponding user within the set historical time period is greater than the set number, the anonymous identity authentication request of the corresponding login ID user is terminated.

[0018] In a possible design, the initial verification information is composed of the login ID and the password data, and a hash value is calculated for the initial verification information to obtain a corresponding target hash value, including:

[0019] Insert each bit of the password data into the login ID at equal intervals, and the number of bits of the insertion interval is N, to form the initial verification information, where the number of bits of the login ID is N times the number of bits of the password data, and N is an integer greater than or equal to 1;

[0020] The MD5 message digest algorithm is used to calculate the hash value of the initial verification information to obtain the corresponding target hash value.

[0021] In one possible design, retrieving the corresponding verification hash value from the first verification library according to the password data includes:

[0022] The first digit of the password data is used as the reference number, and starting from the second digit of the password data, the absolute value of the reference number is subtracted from each digit as the update number of the corresponding digit;

[0023] Keep the first digit of the password data unchanged, and replace each digit starting from the second digit of the password data with the corresponding update digit to obtain the associated data corresponding to the password data;

[0024] The verification hash value associated with the associated data is retrieved from the first verification library.

[0025] In a possible design, calculating the vector association parameter between the biometric feature vector and the verification feature vector, and determining the corresponding distance threshold according to the vector association parameter, includes:

[0026] Determine the n-dimensional biometric vector X = (x 1 , x 2 , …, x n ), and the n-dimensional check feature vector Y = (y 1 ,y 2 , …, y n );

[0027] Substitute the biological feature vector X and the verification feature vector Y into the Euclidean distance calculation formula to calculate and obtain the Euclidean distance between the biological feature vector X and the verification feature vector Y. The Euclidean distance calculation formula is:

[0028]

[0029] Among them, d is the Euclidean distance, and i is the corresponding vector dimension number;

[0030] The Euclidean distance d between the biological feature vector X and the verification feature vector Y is used as the vector association parameter D;

[0031] Substitute the vector association parameter D into the preset distance threshold calculation formula to calculate the corresponding distance threshold. The distance threshold calculation formula is:

[0032]

[0033] Where Z is the distance threshold, is the rounding up operator, S is the number of characters in the check string, σ is the set constant, and min is the minimum value operator.

[0034] In a possible design, the desensitized personal information is subjected to data conversion processing to obtain a corresponding desensitized character string, including:

[0035] Perform base conversion on the desensitized personal information to obtain a corresponding hexadecimal string;

[0036] Perform string shift processing on the hexadecimal string based on the set shift rule to obtain the corresponding desensitized string.

[0037] In a possible design, the data mask is used to extract characters from the desensitized character string, and the extracted characters are combined to obtain a combined character string, including:

[0038] Determine the number of digits of each spare character in the data mask, and use the number of digits of each spare character as the corresponding number of digits for character extraction;

[0039] The corresponding characters are extracted from the desensitized string according to the number of digits of each character extraction, and the extracted characters are combined in the order of the number of digits of the corresponding characters extraction to obtain a combined string.

[0040] In a second aspect, an anonymous identity authentication system based on privacy computing is provided, including an information collection unit, a hash calculation unit, an initial verification unit, a configuration retrieval unit, an association calculation unit, a data conversion unit, a character extraction unit, a distance determination unit and an authentication judgment unit, wherein:

[0041] An information collection unit, used to collect biometric information of the user and obtain anonymous authentication request information submitted by the user, wherein the biometric information includes a biometric vector, and the anonymous authentication request information includes a login ID, password data, and desensitized personal information;

[0042] A hash calculation unit, used to form initial verification information using login ID and password data, and perform hash value calculation on the initial verification information to obtain a corresponding target hash value;

[0043] An initial verification unit, used to retrieve a corresponding verification hash value from the first verification library according to the password data, and use the verification hash value to perform consistency verification on the target hash value. If the target hash value is inconsistent with the verification hash value, it is determined that the user anonymous identity authentication fails;

[0044] A retrieving unit is configured to retrieve the verification feature vector corresponding to the password data from the feature library and retrieve the data mask and verification string corresponding to the password data from the second verification library when the target hash value is consistent with the verification hash value;

[0045] An association calculation unit, used to calculate a vector association parameter between the biological feature vector and the verification feature vector, and determine a corresponding distance threshold according to the vector association parameter;

[0046] A data conversion unit, used to perform data conversion processing on the desensitized personal information to obtain a corresponding desensitized character string;

[0047] A character extraction unit, used to extract characters from the desensitized character string using a data mask, and combine the extracted characters to obtain a combined character string;

[0048] A distance determination unit, used to determine the Hamming distance between the combined character string and the check character string;

[0049] The authentication determination unit is used to determine that the user anonymous identity authentication is passed when the Hamming distance is less than a distance threshold, otherwise, determine that the user anonymous identity authentication is failed.

[0050] Third, an anonymous identity authentication system based on privacy computing is provided, including:

[0051] A memory for storing instructions;

[0052] A processor is used to read the instructions stored in the memory and execute any one of the methods described in the first aspect according to the instructions.

[0053] In a fourth aspect, a computer-readable storage medium is provided, wherein instructions are stored on the computer-readable storage medium, and when the instructions are executed on a computer, the computer executes any one of the methods described in the first aspect. In addition, a computer program product is provided, and when the computer program product is executed on a computer, the computer executes any one of the methods described in the first aspect.

[0054] Beneficial effects: The present invention collects the user's biometric information and anonymous authentication request information, calculates the hash value using the login ID and password data to perform hash value verification, and after the hash value verification passes, calculates the vector association parameter between the biometric vector and the verification feature vector to determine the distance threshold, and then performs data conversion processing and character extraction and combination on the desensitized personal information to obtain a combined string, and finally determines whether the user's anonymous identity authentication passes based on the Hamming distance and distance threshold between the combined string and the verification string, so as to achieve efficient anonymous identity authentication based on privacy computing. The present invention can effectively realize user anonymous identity authentication through corresponding anonymous credential calculation and verification, prevent identity fraud and user personal privacy leakage, and ensure the reliability and stability of user identity authentication. In addition, the biometric information and desensitized personal information used can be set according to actual needs, and the scalability is strong. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0056] Figure 1 This is a schematic diagram of the steps of the method in Example 1 of the present invention;

[0057] Figure 2 This is a schematic diagram of the structure of the system in Example 2 of the present invention;

[0058] Figure 3 Schematic diagram of the system structure in Example 3 of the present invention. DETAILED DESCRIPTION

[0059] It should be noted that the description of these embodiments is used to help understand the present invention, but does not constitute a limitation of the present invention. The specific structures and functional details disclosed herein are only used to describe the exemplary embodiments of the present invention. However, the present invention can be embodied in many alternative forms, and it should not be understood that the present invention is limited to the embodiments set forth herein.

[0060] It should be understood that, unless otherwise clearly specified and limited, the term "connection" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the embodiments can be understood according to specific circumstances.

[0061] In the following description, certain details are provided to facilitate a complete understanding of the example embodiments. However, it will be appreciated by those of ordinary skill in the art that the example embodiments may be implemented without these certain details. For example, devices may be shown in block diagrams to avoid obscuring the examples with unnecessary details. In other embodiments, well-known processes, structures, and techniques may not be shown in unnecessary detail to avoid obscuring the embodiments.

[0062] Embodiment 1:

[0063] This embodiment provides an anonymous identity authentication method based on privacy computing, which can be applied to corresponding identity authentication servers, such as Figure 1 As shown, the method comprises the following steps:

[0064] S1. Collect the user's biometric information and obtain the anonymous authentication request information submitted by the user, wherein the biometric information includes a biometric vector, and the anonymous authentication request information includes a login ID, password data, and desensitized personal information.

[0065] In specific implementation, the user can log in to the account on the user side and input the corresponding password data and desensitized personal information. The desensitized personal information can be the desensitized personal name + mobile phone number, etc., such as "Wang*15*1234**56". The desensitized personal information does not disclose the user's personal privacy and can also be used as the user's unique identifier for subsequent anonymous authentication. At the same time, the user side can synchronously collect the user's biometric information, such as collecting the user's facial feature vector or fingerprint feature vector. The user's biometric vector is a kind of implicit information, which also does not disclose the user's personal privacy and can be used as the user's unique identifier for subsequent anonymous authentication. The user side uploads the collected biometric information of the user to the identity authentication server, and encapsulates the login ID of the user account, the password data input by the user, and the desensitized personal information into an anonymous authentication request message. After adding a timestamp to the anonymous authentication request message, it is synchronously uploaded to the identity authentication server for subsequent anonymous authentication processing.

[0066] S2. Use the login ID and password data to form initial verification information, and calculate the hash value of the initial verification information to obtain the corresponding target hash value.

[0067] In specific implementation, the identity authentication server can first determine the number of anonymous identity authentication requests of the corresponding user within the set historical time period according to the timestamp and the login ID. When the number of anonymous identity authentication requests of the corresponding user within the set historical time period is greater than the set number, it indicates that there is an abnormal repeated authentication application process, and the identity authentication server will terminate the anonymous identity authentication request of the user with the corresponding login ID this time. If the number of anonymous identity authentication requests of the corresponding user within the set historical time period does not exceed the set number, the identity authentication server will start the anonymous authentication process. During the process, the identity authentication server inserts each bit of the password data into the login ID at equal intervals in turn, and the inserted interval bits are N. The number of bits of the login ID is N times the number of bits of the password data, and N is an integer greater than or equal to 1. Then, the MD5 message digest algorithm is used to calculate the hash value of the initial verification information to obtain the corresponding target hash value.

[0068] S3. Retrieve the corresponding verification hash value from the first verification library according to the password data, and use the verification hash value to perform consistency verification on the target hash value. If the target hash value is inconsistent with the verification hash value, it is determined that the user's anonymous identity authentication fails.

[0069] In specific implementation, the identity authentication server first uses the first digit of the password data as a reference number, and starting from the second digit of the password data, uses the absolute value of each digit minus the reference number as the update number of the corresponding number; then keeps the first digit of the password data unchanged, and starting from the second digit of the password data, replaces each digit with the corresponding update number to obtain the associated data corresponding to the password data; then retrieves the verification hash value associated with the associated data from the first verification library; finally, uses the retrieved verification hash value to perform a consistency check on the target hash value. If the target hash value is inconsistent with the verification hash value, it is determined that the user's anonymous identity authentication has failed.

[0070] S4. When the target hash value is consistent with the verification hash value, the verification feature vector corresponding to the password data is retrieved from the feature library, and the data mask and verification string corresponding to the password data are retrieved from the second verification library.

[0071] During specific implementation, if the target hash value is consistent with the verification hash value, the identity authentication server continues with subsequent processing, that is, processes the password data in the same way as in step S3 to obtain associated data, and then retrieves the corresponding verification feature vector from the feature library based on the associated data, and retrieves the corresponding data mask and verification string from the second verification library.

[0072] S5. Calculate the vector association parameter between the biological feature vector and the verification feature vector, and determine the corresponding distance threshold according to the vector association parameter.

[0073] In specific implementation, the identity authentication server determines the n-dimensional biometric feature vector X=(x 1 , x 2 , …, x n ), and the n-dimensional check feature vector Y = (y 1 ,y 2 , …, y n );

[0074] Substitute the biological feature vector X and the verification feature vector Y into the Euclidean distance calculation formula to calculate and obtain the Euclidean distance between the biological feature vector X and the verification feature vector Y. The Euclidean distance calculation formula is:

[0075]

[0076] Among them, d is the Euclidean distance, and i is the corresponding vector dimension number;

[0077] The Euclidean distance d between the biological feature vector X and the verification feature vector Y is used as the vector association parameter D;

[0078] Substitute the vector association parameter D into the preset distance threshold calculation formula to calculate the corresponding distance threshold. The distance threshold calculation formula is:

[0079]

[0080] Where Z is the distance threshold, is the rounding up operator, S is the number of characters in the check string, σ is the set constant, and min is the minimum value operator.

[0081] S6. Perform data conversion processing on the desensitized personal information to obtain a corresponding desensitized character string.

[0082] In specific implementation, the identity authentication server first performs base conversion processing on the binary desensitized personal information to obtain a corresponding hexadecimal string, and then performs string shift processing on the hexadecimal string based on the set shift rule to obtain a corresponding desensitized string.

[0083] S7. Use the data mask to extract characters from the desensitized character string, and combine the extracted characters to obtain a combined character string.

[0084] In the specific implementation, the identity authentication server first determines the number of spare character digits in the data mask, and uses the number of spare character digits as the corresponding number of character extraction digits, then extracts the corresponding characters from the desensitized string according to the number of character extraction digits, and combines the extracted characters in the order of the corresponding character extraction digits to obtain a combined string.

[0085] S8. Determine the Hamming distance between the combined string and the check string.

[0086] In specific implementation, the identity authentication server determines the Hamming distance between the combined string and the verification string. The Hamming distance represents the number of different characters in the corresponding positions of two (same length) strings. When performing statistics, the two strings are XORed and the number of results of 1 is counted, and this number is used as the Hamming distance between the two strings. The verification string is the corresponding string obtained by the identity authentication server when the user first registers. The corresponding characters in the string are extracted in the same way. It can be used as a standard string for anonymous authentication of users.

[0087] S9. When the Hamming distance is less than the distance threshold, it is determined that the user anonymous identity authentication is passed; otherwise, it is determined that the user anonymous identity authentication is failed.

[0088] In specific implementation, when the Hamming distance between the combined string and the verification string is less than the distance threshold, the identity authentication server determines that the user's anonymous identity authentication is successful. Conversely, when the Hamming distance between the combined string and the verification string is greater than or equal to the distance threshold, the identity authentication server determines that the user's anonymous identity authentication is unsuccessful.

[0089] The method of this embodiment can effectively realize anonymous user identity authentication through corresponding anonymous credential calculation and verification, prevent identity fraud and user privacy leakage, and ensure the reliability and stability of user identity authentication. In addition, the biometric information and desensitized personal information used can be set according to actual needs, and the scalability is strong.

[0090] Embodiment 2:

[0091] This embodiment provides an anonymous identity authentication system based on privacy computing, such as Figure 2 As shown, it includes an information collection unit, a hash calculation unit, an initial verification unit, a configuration retrieval unit, an association calculation unit, a data conversion unit, a character extraction unit, a distance determination unit and an authentication judgment unit, wherein:

[0092] An information collection unit, used to collect biometric information of the user and obtain anonymous authentication request information submitted by the user, wherein the biometric information includes a biometric vector, and the anonymous authentication request information includes a login ID, password data, and desensitized personal information;

[0093] A hash calculation unit, used to form initial verification information using login ID and password data, and perform hash value calculation on the initial verification information to obtain a corresponding target hash value;

[0094] An initial verification unit, used to retrieve a corresponding verification hash value from the first verification library according to the password data, and use the verification hash value to perform consistency verification on the target hash value. If the target hash value is inconsistent with the verification hash value, it is determined that the user anonymous identity authentication fails;

[0095] A retrieving unit is configured to retrieve the verification feature vector corresponding to the password data from the feature library and retrieve the data mask and verification string corresponding to the password data from the second verification library when the target hash value is consistent with the verification hash value;

[0096] An association calculation unit, used to calculate a vector association parameter between the biological feature vector and the verification feature vector, and determine a corresponding distance threshold according to the vector association parameter;

[0097] A data conversion unit, used to perform data conversion processing on the desensitized personal information to obtain a corresponding desensitized character string;

[0098] A character extraction unit, used to extract characters from the desensitized character string using a data mask, and combine the extracted characters to obtain a combined character string;

[0099] A distance determination unit, used to determine the Hamming distance between the combined character string and the check character string;

[0100] The authentication determination unit is used to determine that the user anonymous identity authentication is passed when the Hamming distance is less than a distance threshold, otherwise, determine that the user anonymous identity authentication is failed.

[0101] Embodiment 3:

[0102] This embodiment provides an anonymous identity authentication system based on privacy computing, such as Figure 3 As shown, at the hardware level, it includes:

[0103] Data interface, used to establish data connection between the processor and the user end;

[0104] A memory for storing instructions;

[0105] The processor is used to read the instructions stored in the memory and execute the anonymous identity authentication method based on privacy computing in Example 1 according to the instructions.

[0106] Optionally, the system further includes an internal bus, through which the processor, the memory and the data interface can be interconnected, and the internal bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into an address bus, a data bus, a control bus, etc.

[0107] The memory may include, but is not limited to, random access memory (RAM), read only memory (ROM), flash memory, first input first output (FIFO) and / or first in last out (FILO) memory, etc. The processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it may also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0108] Embodiment 4:

[0109] This embodiment provides a computer-readable storage medium, on which instructions are stored, and when the instructions are executed on a computer, the computer executes the anonymous identity authentication method based on privacy computing in Embodiment 1. The computer-readable storage medium refers to a carrier for storing data, which may include but is not limited to a floppy disk, an optical disk, a hard disk, a flash memory, a USB flash drive and / or a memory stick, etc., and the computer may be a general-purpose computer, a special-purpose computer, a computer network or other programmable devices.

[0110] This embodiment also provides a computer program product, which, when running on a computer, executes the anonymous identity authentication method based on privacy computing in Embodiment 1. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.

[0111] Finally, it should be noted that the above description is only a preferred embodiment of the present invention and is not intended to limit the protection scope of the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An anonymous identity authentication method based on privacy computing, characterized in that: include: Collecting the user's biometric information and obtaining anonymous authentication request information submitted by the user, wherein the biometric information includes a biometric vector, and the anonymous authentication request information includes a login ID, password data, and desensitized personal information; The initial verification information is composed of the login ID and the password data, and a hash value is calculated for the initial verification information to obtain a corresponding target hash value, wherein the initial verification information is composed of the login ID and the password data, and a hash value is calculated for the initial verification information to obtain a corresponding target hash value, including: inserting each bit of the password data into the login ID in turn at equal intervals, and the number of insertion intervals is N to compose the initial verification information, wherein the number of bits of the login ID is N times the number of bits of the password data, and N is an integer greater than or equal to 1; and the initial verification information is calculated by using the MD5 message digest algorithm to obtain a corresponding target hash value; Retrieving the corresponding verification hash value from the first verification library according to the password data, and using the verification hash value to perform consistency verification on the target hash value. If the target hash value is inconsistent with the verification hash value, it is determined that the user anonymous identity authentication fails; the retrieving the corresponding verification hash value from the first verification library according to the password data includes: taking the first digit of the password data as a reference number, starting from the second digit of the password data, using the absolute value of the reference number subtracted from each digit as the update number of the corresponding number; keeping the first digit of the password data unchanged, starting from the second digit of the password data, replacing each digit with the corresponding update number, and obtaining the associated data corresponding to the password data; retrieving the verification hash value associated with the associated data from the first verification library; When the target hash value is consistent with the verification hash value, the verification feature vector corresponding to the password data is retrieved from the feature library, and the data mask and verification string corresponding to the password data are retrieved from the second verification library; Calculating a vector correlation parameter between a biological feature vector and a verification feature vector, and determining a corresponding distance threshold value according to the vector correlation parameter; the calculating a vector correlation parameter between a biological feature vector and a verification feature vector, and determining a corresponding distance threshold value according to the vector correlation parameter, comprises: Determine the n-dimensional biometric vector X = ( x1, x2, …, x n) , and the n-dimensional check feature vector Y = ( y1,y2,…,y n ) ; Substitute the biological feature vector X and the verification feature vector Y into the Euclidean distance calculation formula to calculate and obtain the Euclidean distance between the biological feature vector X and the verification feature vector Y. The Euclidean distance calculation formula is: Among them, d is the Euclidean distance, and i is the corresponding vector dimension number; The Euclidean distance d between the biological feature vector X and the verification feature vector Y is used as the vector association parameter D; Substitute the vector association parameter D into the preset distance threshold calculation formula to calculate the corresponding distance threshold. The distance threshold calculation formula is: Where Z is the distance threshold, is the rounding up operator, S is the number of characters in the check string, σ is the set constant, and min is the minimum value operator; Perform data conversion processing on the desensitized personal information to obtain the corresponding desensitized character string; Using data mask to extract characters from the desensitized character string, and combining the extracted characters to obtain a combined character string; Determine the Hamming distance between the combined string and the check string; When the Hamming distance is less than the distance threshold, it is determined that the user anonymous identity authentication is passed; otherwise, it is determined that the user anonymous identity authentication is failed.

2. The anonymous identity authentication method based on privacy computing according to claim 1 is characterized in that: The anonymous authentication request information also includes a timestamp. Before the login ID and password data are used to form the initial verification information, the method further includes: Determine the number of anonymous identity authentication requests made by the corresponding user within a set historical time period based on the timestamp and login ID; When the number of anonymous identity authentication requests of the corresponding user within the set historical time period is greater than the set number, the anonymous identity authentication request of the corresponding login ID user is terminated.

3. The anonymous identity authentication method based on privacy computing according to claim 1 is characterized in that: The data conversion process of the desensitized personal information is performed to obtain a corresponding desensitized character string, including: Perform base conversion on the desensitized personal information to obtain a corresponding hexadecimal string; Perform string shift processing on the hexadecimal string based on the set shift rule to obtain the corresponding desensitized string.

4. The anonymous identity authentication method based on privacy computing according to claim 1 is characterized in that: The method of extracting characters from the desensitized character string by using a data mask and combining the extracted characters to obtain a combined character string includes: Determine the number of digits of each spare character in the data mask, and use the number of digits of each spare character as the corresponding number of digits for character extraction; The corresponding characters are extracted from the desensitized string according to the number of digits of each character extraction, and the extracted characters are combined in the order of the number of digits of the corresponding characters extraction to obtain a combined string.

5. An anonymous identity authentication system based on privacy computing, characterized in that: It includes an information collection unit, a hash calculation unit, an initial verification unit, a configuration retrieval unit, an association calculation unit, a data conversion unit, a character extraction unit, a distance determination unit and an authentication judgment unit, wherein: An information collection unit, used to collect biometric information of the user and obtain anonymous authentication request information submitted by the user, wherein the biometric information includes a biometric vector, and the anonymous authentication request information includes a login ID, password data, and desensitized personal information; A hash calculation unit, used to form initial verification information using login ID and password data, and perform hash value calculation on the initial verification information to obtain a corresponding target hash value, wherein the use of login ID and password data to form initial verification information, and the hash value calculation on the initial verification information to obtain a corresponding target hash value, comprises: inserting each bit of the password data into the login ID in turn at equal intervals, and the number of insertion intervals is N, to form the initial verification information, wherein the number of bits of the login ID is N times the number of bits of the password data, and N is an integer greater than or equal to 1; and performing hash value calculation on the initial verification information using the MD5 message digest algorithm to obtain a corresponding target hash value; The initial verification unit is used to retrieve the corresponding verification hash value from the first verification library according to the password data, and use the verification hash value to perform consistency verification on the target hash value. If the target hash value is inconsistent with the verification hash value, it is determined that the user anonymous identity authentication fails; the corresponding verification hash value is retrieved from the first verification library according to the password data, including: taking the first digit of the password data as a reference number, starting from the second digit of the password data, using the absolute value of the reference number subtracted from each digit as the update number of the corresponding number; keeping the first digit of the password data unchanged, starting from the second digit of the password data, replacing each digit with the corresponding update number, and obtaining the associated data corresponding to the password data; retrieving the verification hash value associated with the associated data from the first verification library; A retrieving unit is configured to retrieve the verification feature vector corresponding to the password data from the feature library and retrieve the data mask and verification string corresponding to the password data from the second verification library when the target hash value is consistent with the verification hash value; The association calculation unit is used to calculate the vector association parameter between the biological feature vector and the verification feature vector, and determine the corresponding distance threshold according to the vector association parameter; the calculation of the vector association parameter between the biological feature vector and the verification feature vector, and determining the corresponding distance threshold according to the vector association parameter, includes: Determine the n-dimensional biometric vector X = (x1, x2, ..., x n ), and the n-dimensional check feature vector Y = (y1, y2, ..., y n ); Substitute the biological feature vector X and the verification feature vector Y into the Euclidean distance calculation formula to calculate and obtain the Euclidean distance between the biological feature vector X and the verification feature vector Y. The Euclidean distance calculation formula is: Among them, d is the Euclidean distance, and i is the corresponding vector dimension number; The Euclidean distance d between the biological feature vector X and the verification feature vector Y is used as the vector association parameter D; Substitute the vector association parameter D into the preset distance threshold calculation formula to calculate the corresponding distance threshold. The distance threshold calculation formula is: Where Z is the distance threshold, is the rounding up operator, S is the number of characters in the check string, σ is the set constant, and min is the minimum value operator; A data conversion unit, used to perform data conversion processing on the desensitized personal information to obtain a corresponding desensitized character string; A character extraction unit, used to extract characters from the desensitized character string using a data mask, and combine the extracted characters to obtain a combined character string; A distance determination unit, used to determine the Hamming distance between the combined character string and the check character string; The authentication determination unit is used to determine that the user anonymous identity authentication is passed when the Hamming distance is less than a distance threshold, otherwise, determine that the user anonymous identity authentication is failed.

6. An anonymous identity authentication system based on privacy computing, characterized in that: include: A memory for storing instructions; A processor is used to read the instructions stored in the memory and execute the anonymous identity authentication method according to any one of claims 1 to 4 according to the instructions.

7. A computer program product, characterized in that When the computer program product is run on a computer, the anonymous identity authentication method according to any one of claims 1 to 4 is executed.

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