A distributed blockchain identity verification system

Abstract

The present invention relates to the field of identity authentication technology, and in particular, to a distributed blockchain identity authentication system. It includes a processing verification unit, a data analysis unit, and a tampering optimization unit. The anomaly detection module of the present invention performs anomaly detection based on processed data, passes the detected processed data into the identity authentication module and performs identity verification, then passes the verified data into the data analysis unit and performs data tampering analysis, passes the analyzed data into the tampering optimization unit, and the tampering optimization unit optimizes data tampering based on the acquired relevant identity data and analyzed data, passes the optimized data into the anomaly detection module and performs anomaly detection again, detects the integrity of identity information in the optimized data, and by re-detecting the integrity of the identity information, it can ensure that the data has not been tampered with or damaged, and prevents the tampered data from misleading or causing risks to identity identification and verification, thereby enhancing data security.

Description

Technical Field

[0001] The present invention relates to the field of identity authentication technology, and in particular to a distributed blockchain identity authentication system. Background Art

[0002] In real-world scenarios, decentralized networks and cryptographic algorithms are basically used to securely verify the identity and permissions of users, thereby improving data security and credibility. When identity information is successfully verified through general biometric technology, there is a risk of data tampering after successful verification, which will affect the integrity of the identity information and lead to data unreliability. In order to ensure that the data has not been tampered with or damaged, and to prevent tampered data from misleading or causing risks to identity identification and verification, we provide a distributed blockchain identity authentication system. Summary of the invention

[0003] The purpose of the present invention is to provide a distributed blockchain identity authentication system to solve the problems raised in the above background technology.

[0004] To achieve the above-mentioned object, the present invention provides a distributed blockchain identity authentication system, including a processing and verification unit, a data analysis unit, and a tampering optimization unit;

[0005] The processing and verification unit is used to obtain and process relevant identity data, and then perform anomaly detection and identity verification based on the processed data;

[0006] The data analysis unit is used to receive the identity verification data in the processing verification unit and perform data tampering analysis;

[0007] The tampering optimization unit is used to receive the relevant identity data obtained in the processing verification unit and the data analyzed in the data analysis unit for tampering optimization;

[0008] The processing and verification unit performs anomaly detection based on the processed data, transmits the anomaly detection data to the data analysis unit and performs data tampering analysis, then transmits the analyzed data to the tampering optimization unit and optimizes the data tampering based on the relevant identity data obtained in the processing and verification unit and the data analyzed in the data analysis unit, and then transmits the optimized data to the processing and verification unit and performs anomaly detection again.

[0009] As a further improvement of the technical solution, the processing verification unit includes an acquisition processing module, an anomaly detection module and an identity authentication module;

[0010] The acquisition and processing module is used to acquire relevant identity data and store it in a database, and then perform processing based on the acquired relevant identity data;

[0011] The anomaly detection module is used to receive the relevant identity data obtained in the acquisition processing module, and use the anomaly processing algorithm to perform anomaly detection based on the obtained relevant identity data, and then compare the hash value obtained by the anomaly processing algorithm with the set hash value. The specific comparison is as follows:

[0012] When the hash value obtained by the exception processing algorithm is not equal to the set hash value, it means that the identity information of the acquired relevant identity data is incomplete, and the detection command is transmitted to the acquisition processing module. The acquisition processing module receives the detection command and transmits the re-acquired relevant identity data to the exception detection module;

[0013] When the hash value obtained by the exception handling algorithm is equal to the set complete data value, the identity information is said to be complete, and the detected data is passed to the identity authentication module;

[0014] The identity verification module is used to receive the data detected in the anomaly detection module, perform identity verification based on the detected data, and transfer the identity verification data to the data analysis unit.

[0015] As a further improvement of the present technical solution, the data analysis unit is used to receive identity authentication data in the identity authentication module, and use a tampering mining algorithm to analyze data tampering based on the identity authentication data, and transmit the analyzed data to the tampering optimization unit.

[0016] As a further improvement of the present technical solution, the tampering optimization unit is used to receive the relevant identity data acquired in the acquisition processing module and the data analyzed in the data analysis unit, and use the tampering optimization algorithm to optimize the data tampering based on the acquired relevant identity data and the analyzed data, and pass the optimized data into the anomaly detection module.

[0017] As a further improvement of the technical solution, the implementation principle of the tampering optimization algorithm formula in the tampering optimization unit is:

[0018] Principle ①: First, use the tampering mining algorithm to derive the tampering chain based on the acquired relevant identity data and analyzed data, and integrate the acquired relevant identity data and analyzed data into data blocks;

[0019] Principle ②: Merge the calculated data tampering value with the tampering value of the previous data block, and then use the merged result as the tampering value of the current data block;

[0020] Principle ③: Finally, the tampered value of the current data block is added to the tampered chain in Principle ① for iterative optimization in sequence to obtain the optimized tampered data.

[0021] As a further improvement of the technical solution, the anomaly detection module receives the data optimized in the tampering optimization unit, and performs anomaly detection based on the optimized data using an anomaly processing algorithm;

[0022] Exception handling algorithm implementation process:

[0023] Process ①: First, the input optimized data block is divided into 512 bits, and the divided data is called a block message. When the length of the block message is less than 512 bits, a complete 512-bit block message is obtained by adding an indefinite number of padding bits at the end;

[0024] Process ②: Process block messages through four rounds of loops, and perform 16 exception handling task operations in each round;

[0025] Process ③: After processing all block messages, the hash value is obtained by using the exception processing algorithm on the block message.

[0026] As a further improvement of the present technical solution, the anomaly detection module performs anomaly detection based on the processed data, passes the detected processed data into the identity authentication module and performs identity verification, then passes the verified data into the data analysis unit and performs data tampering analysis, passes the analyzed data into the tampering optimization unit, the tampering optimization unit optimizes the data tampering based on the acquired relevant identity data and the analyzed data, passes the optimized data into the anomaly detection module and performs anomaly detection again, and detects the integrity of the identity information in the optimized data.

[0027] Compared with the prior art, the present invention has the following beneficial effects:

[0028] In the distributed blockchain identity authentication system, the anomaly detection module performs anomaly detection based on processed data, passes the detected processed data into the identity authentication module and performs identity verification, and then passes the verified data into the data analysis unit and performs data tampering analysis. The tampered data may affect the integrity of the identity information and may cause the data to be unreliable. The analyzed data is passed into the tampering optimization unit, and the tampering optimization unit optimizes the data tampering based on the acquired relevant identity data and the analyzed data, and passes the optimized data into the anomaly detection module and performs anomaly detection again, and detects the integrity of the identity information in the optimized data. By re-detecting the integrity of the identity information, it can be ensured that the data has not been tampered with or damaged, and prevent the tampered data from misleading or causing risks to identity identification and verification, thereby enhancing data security. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 is an overall block diagram of the present invention;

[0030] Figure 2 It is a block diagram of the real-time detection unit of the present invention.

[0031] The meaning of each number in the figure is:

[0032] 1. Processing verification unit; 11. Acquisition processing module; 12. Anomaly detection module; 13. Identity authentication module;

[0033] 2. Data analysis unit; 3. Tampering optimization unit. DETAILED DESCRIPTION

[0034] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0035] Example 1

[0036] Considering that there is a risk of data tampering after successful verification, the integrity of the identity information will be affected, resulting in data unreliability. In order to ensure that the data has not been tampered with or damaged, and to prevent the tampered data from misleading or causing risks to identity identification and verification, we provide a distributed blockchain identity authentication system.

[0037] The present invention provides a distributed blockchain identity authentication system, please refer to Figure 1-Figure 2 , including a processing verification unit 1, a data analysis unit 2, and a tampering optimization unit 3;

[0038] The processing and verification unit 1 is used to obtain relevant identity data and process it, and then perform anomaly detection and identity authentication based on the processed data. The data analysis unit 2 is used to receive the identity authentication data in the processing and verification unit 1 and perform data tampering analysis. The tampering optimization unit 3 is used to receive the relevant identity data obtained in the processing and verification unit 1 and the data analyzed in the data analysis unit 2 to optimize tampering.

[0039] The processing verification unit 1 performs anomaly detection based on the processed data, transmits the anomaly detection data to the data analysis unit 2 and performs data tampering analysis, then transmits the analyzed data to the tampering optimization unit 3 and optimizes the data tampering based on the relevant identity data obtained in the processing verification unit 1 and the data analyzed in the data analysis unit 2, transmits the optimized data to the processing verification unit 1 and performs anomaly detection again. By re-detecting the integrity of the identity information, the user's trust and satisfaction with the data can be improved, and the user's confidence in the data processing process can be enhanced.

[0040] The following is a refinement of the above units, see Figure 1-Figure 2;

[0041] The processing and verification unit 1 includes an acquisition processing module 11, an abnormality detection module 12 and an identity authentication module 13;

[0042] The acquisition processing module 11 is used to acquire relevant identity data and store it in a database, and then perform processing based on the acquired relevant identity data. This processing is mainly used to reduce data errors, and the processed data is transmitted to the anomaly detection module 12. The anomaly detection module 12 is used to receive the relevant identity data acquired in the acquisition processing module 11, and use the anomaly processing algorithm to perform anomaly detection based on the acquired relevant identity data, and then compare the hash value obtained by the anomaly processing algorithm with the set hash value;

[0043] Exception handling algorithm implementation process:

[0044] Process 1: First, the input processed data block is divided into 512 bits, and the divided data is called a block message. When the length of the block message is less than 512 bits, a complete 512-bit block message is obtained by adding an indefinite number of padding bits at the end;

[0045] Process 2: Process block messages through four rounds of loops, with 16 exception handling task operations performed in each round;

[0046] Process 3: After processing all block messages, a hash value is obtained by applying an exception processing algorithm to the block messages.

[0047] Specific comparison:

[0048] When the hash value obtained by the exception processing algorithm is not equal to the set hash value, it means that the identity information of the acquired relevant identity data is incomplete, and the detection command is transmitted to the acquisition processing module 11, and the acquisition processing module 11 receives the detection command and transmits the re-acquired relevant identity data to the exception detection module 12;

[0049] When the hash value obtained by the exception handling algorithm is equal to the set complete data value, it means that the identity information is complete, and the detected data is transmitted to the identity authentication module 13.

[0050] The identity authentication module 13 is used to receive the data detected by the anomaly detection module 12 and perform identity verification based on the detected data. The specific verification situation is:

[0051] Case 1: When the detected identity information is exactly the same as the matched identity information in the database, the verification is successful, and the identity verification data is transmitted to the data analysis unit 2;

[0052] Case 2: When the detected identity information is different from the matched identity information in the database, the verification fails, and the verification command is passed to the acquisition processing module 11. The acquisition processing module 11 receives the verification command and passes the newly acquired related identity data to the anomaly detection module 12.

[0053] The data analysis unit 2 is used to receive the identity authentication data in the identity authentication module 13, and use the tampering mining algorithm to analyze the data tampering according to the identity authentication data;

[0054] Tampering with the mining algorithm formula:

[0055] S=∑(W T +b)*[(x1+x2+...+x n ) / n];

[0056] Among them, S represents the tampered data value to be analyzed, W refers to the data value of the authentication, T refers to the time value generated when the authentication data is received, b refers to the weight vector value, x1, x2, ..., x n Refers to the different variable values ​​generated during the authentication data process, and n refers to the quantity (constant);

[0057] When S≤1, it means that the analyzed tampered data value is calculated incorrectly, and the analyzed command is transmitted to the identity authentication module 13. The identity authentication module 13 receives the analyzed command and re-transmits the identity authentication data to the data analysis unit 2, and the data analysis unit 2 recalculates;

[0058] When S≥1, it indicates that the risk of identity information being tampered is detected, and the analyzed data is transmitted to the tampering optimization unit 3;

[0059] This formula is mainly used for data tampering analysis. By analyzing the tampering of identity authentication data, unusual patterns or behaviors can be identified, and possible fraud, impersonation and other malicious behaviors can be discovered in a timely manner, thereby improving security and risk prevention capabilities.

[0060] The tampering optimization unit 3 is used to receive the relevant identity data obtained in the acquisition processing module 11 and the data analyzed in the data analysis unit 2, and optimize the data tampering according to the obtained relevant identity data and the analyzed data using the tampering optimization algorithm. By adopting data security measures and technologies, the integrity and credibility of the data can be ensured to prevent the data from being maliciously tampered, modified or destroyed, and the optimized data is transmitted to the anomaly detection module 12;

[0061] The implementation principle of the tampering optimization algorithm formula in tampering optimization unit 3:

[0062] Principle ①: First, use the tampering mining algorithm to derive the tampering chain based on the acquired relevant identity data and analyzed data, and integrate the acquired relevant identity data and analyzed data into data blocks;

[0063] Principle ②: Merge the calculated data tampering value with the tampering value of the previous data block, and then use the merged result as the tampering value of the current data block;

[0064] Principle ③: Finally, the tampered value of the current data block is added to the tampered chain in Principle ① for iterative optimization in sequence to obtain the optimized tampered data.

[0065] The anomaly detection module 12 receives the data optimized in the tampering optimization unit 3, and uses the anomaly processing algorithm to perform anomaly detection based on the optimized data. By performing anomaly detection again, the quality of the data can be further improved, the impact of potential errors or abnormal data can be reduced, and the accuracy and reliability of the data can be improved;

[0066] Exception handling algorithm implementation process:

[0067] Process ①: First, the input optimized data block is divided into 512 bits, and the divided data is called a block message. When the length of the block message is less than 512 bits, a complete 512-bit block message is obtained by adding an indefinite number of padding bits at the end;

[0068] Process ②: Process block messages through four rounds of loops, and perform 16 exception handling task operations in each round;

[0069] Process ③: After processing all block messages, the hash value is obtained by using the exception processing algorithm on the block message.

[0070] The anomaly detection module 12 performs anomaly detection based on the processed data, passes the detected processed data into the identity authentication module 13 and performs identity verification, then passes the verified data into the data analysis unit 2 and performs data tampering analysis, and passes the analyzed data into the tampering optimization unit 3. The tampering optimization unit 3 optimizes the data tampering based on the acquired relevant identity data and the analyzed data, passes the optimized data into the anomaly detection module 12 and performs anomaly detection again to detect the integrity of the identity information in the optimized data.

[0071] Usage process:

[0072] The anomaly detection module 12 performs anomaly detection based on the processed data, passes the detected processed data into the identity authentication module 13 and performs identity verification, then passes the verified data into the data analysis unit 2 and performs data tampering analysis, passes the analyzed data into the tampering optimization unit 3 and optimizes the data tampering based on the acquired relevant identity data and the analyzed data, passes the optimized data into the anomaly detection module 12 and performs anomaly detection again, detects the integrity of the identity information in the optimized data, passes the re-detected data into the identity authentication module 13 and performs identity verification again.

[0073] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and descriptions are only preferred examples of the present invention and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A distributed blockchain identity authentication system, characterized by: It includes a processing verification unit (1), a data analysis unit (2), and a tampering optimization unit (3); The processing and verification unit (1) is used to obtain relevant identity data and process it, and then perform anomaly detection and identity verification based on the processed data; The data analysis unit (2) is used to receive and process the identity verification data in the verification unit (1) and to analyze the data for tampering; The tampering optimization unit (3) is used to receive the relevant identity data obtained by the processing verification unit (1) and the data analyzed by the data analysis unit (2) to perform tampering optimization; The processing verification unit (1) performs anomaly detection based on the processed data, transmits the anomaly detected data to the data analysis unit (2) and performs data tampering analysis, then transmits the analyzed data to the tampering optimization unit (3) and optimizes the data tampering based on the relevant identity data obtained in the processing verification unit (1) and the data analyzed in the data analysis unit (2), transmits the optimized data to the processing verification unit (1) and performs anomaly detection again; The processing and verification unit (1) comprises an acquisition processing module (11), an abnormality detection module (12) and an identity verification module (13); The acquisition and processing module (11) is used to acquire relevant identity data and store it in a database, and then perform processing based on the acquired relevant identity data; The anomaly detection module (12) is used to receive the relevant identity data obtained in the acquisition processing module (11), and use the anomaly processing algorithm to perform anomaly detection based on the obtained relevant identity data, and then compare the hash value obtained by the anomaly processing algorithm with the set hash value. The specific comparison is as follows: When the hash value obtained by the exception processing algorithm is not equal to the set hash value, it means that the identity information of the acquired relevant identity data is incomplete, and the detection command is transmitted to the acquisition processing module (11). The acquisition processing module (11) receives the detection command and transmits the re-acquired relevant identity data to the exception detection module (12); When the hash value obtained by the exception handling algorithm is equal to the set complete data value, the identity information is said to be complete, and the detected data is passed to the identity authentication module (13); The identity verification module (13) is used to receive the data detected by the anomaly detection module (12), perform identity verification based on the detected data, and transmit the identity verification data to the data analysis unit (2); The data analysis unit (2) is used to receive identity authentication data from the identity authentication module (13), and use a tampering mining algorithm to perform data tampering analysis based on the identity authentication data, and transmit the analyzed data to the tampering optimization unit (3); The tampering optimization unit (3) is used to receive the relevant identity data acquired in the acquisition processing module (11) and the data analyzed in the data analysis unit (2), and optimize the data tampering according to the acquired relevant identity data and the analyzed data using a tampering optimization algorithm, and transmit the optimized data to the anomaly detection module (12); The implementation principle of the tampering optimization algorithm formula in the tampering optimization unit (3) is: Principle ①: First, use the tampering mining algorithm to derive the tampering chain based on the acquired relevant identity data and analyzed data, and integrate the acquired relevant identity data and analyzed data into data blocks; Principle ②: Merge the calculated data tampering value with the tampering value of the previous data block, and then use the merged result as the tampering value of the current data block; Principle ③: Finally, the tampered value of the current data block is added to the tampered chain in Principle ① for iterative optimization in sequence to obtain the optimized tampered data; The anomaly detection module (12) receives the data optimized in the tampering optimization unit (3), and uses an anomaly processing algorithm to perform anomaly detection based on the optimized data; Exception handling algorithm implementation process: Process ①: First, the input optimized data block is divided into 512 bits, and the divided data is called a block message. When the length of the block message is less than 512 bits, a complete 512-bit block message is obtained by adding an indefinite number of padding bits at the end; Process ②: Process block messages through four rounds of loops, and perform 16 exception handling task operations in each round; Process ③: After processing all block messages, the hash value is obtained by using the exception processing algorithm on the block message.

2. The distributed blockchain identity authentication system according to claim 1, characterized in that: The anomaly detection module (12) performs anomaly detection based on the processed data, transmits the detected processed data to the identity authentication module (13) and performs identity verification, then transmits the verified data to the data analysis unit (2) and performs data tampering analysis, transmits the analyzed data to the tampering optimization unit (3), the tampering optimization unit (3) optimizes data tampering based on the acquired relevant identity data and the analyzed data, transmits the optimized data to the anomaly detection module (12) and performs anomaly detection again, and detects the integrity of the identity information in the optimized data.

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