A credit data security management system based on cloud services

Abstract

A credit data security management system based on cloud service, relating to the technical field of cloud service, wherein the cloud server is communicatively connected with a data encryption module, a data decryption module, a cross-verification module, a data chain module and a secondary identity authentication module; the cloud server is used to build a credit data alliance sharing platform, taking the affiliated credit terminals as blockchain nodes, and collecting the credit data uploaded by the affiliated credit terminals; the data encryption module is used to encrypt the credit data and generate an encrypted data packet; the data decryption module is used to decrypt the encrypted data packet transmitted to the blockchain node; the cross-verification module is used to perform data cross-verification on the credit feature vector matrix with the credit feature vector matrix stored in each blockchain node in the blockchain network; the data chain module is used to perform data chain operation; the secondary identity authentication module is used to perform secondary identity authentication on the credit data, significantly enhancing the accuracy, timeliness and predictability of the credit data.

Description

Technical Field

[0001] The present invention relates to the technical field of cloud services, and in particular to a credit information data security management system based on cloud services. Background Art

[0002] In recent years, my country's Internet has developed rapidly, and the development of modern information technologies such as mobile payment, social network, cloud computing and big data has had a huge impact on the existing credit reporting model. At the same time, it has also brought many challenges to the credit evaluation, which is the most important part of the credit reporting market. Traditional offline credit reporting methods are single and have limited data sources. They cannot effectively deal with the risks brought about by credit reporting innovation, which limits the willingness of financial institutions to carry out financial innovation and has an adverse impact on the development of the real economy.

[0003] The existing technology CN114372251B "Credit data security and privacy protection method" includes: uploading encrypted data to the node, the node re-encrypts the encrypted data, and the node verifies the terminal when receiving the data acquisition request. If the verification is successful, the data between nodes is sent to the terminal. If the blockchain determines that the node is untrustworthy, the blockchain abandons the node and transfers the data and logs stored in the node. The present invention applies blockchain technology to the credit system service, so that each credit agency can share credit data on the basis of non-leakage of credit data, and ensure the security of credit data. At the same time, the present invention can more effectively protect individual credit data from the individual level by using secondary encryption for data, thereby further ensuring the security of blockchain for corporate information storage;

[0004] Prior art CN114021164B "Privacy protection method for credit reporting system based on blockchain" This method realizes the secure sharing of credit reporting data among multiple entities such as credit reporting users, credit reporting agencies and cloud service providers, and ensures the confidentiality, availability, tamper-proofness and ciphertext retrievability of credit reporting data, as well as fairness and identity authentication anonymity in credit reporting data query. Based on blockchain and smart contracts, the problem of credit reporting data isolation is solved; based on zero-knowledge proof, anonymous identity authentication is realized without leaking the privacy of credit reporting users; based on searchable symmetric encryption technology, the ciphertext of credit reporting data can be retrieved.

[0005] Judging from the actual situation of existing credit reporting technologies, since it involves the core user data of enterprises, it is difficult to achieve information sharing in the absence of an effective mechanism. Different enterprises often establish different platforms, and thus lack a unified data platform. It is impossible to give full play to the advantages of Internet credit reporting and it is difficult to achieve the sharing and utilization of credit information on a larger scale. In order to solve the above technical problems, a credit data security management system based on cloud services is now provided. Summary of the invention

[0006] In order to solve the above technical problems, the object of the present invention is to provide a credit data security management system based on cloud services, including a cloud server, wherein the cloud server is communicatively connected with a data encryption module, a data decryption module, a cross-verification module, a data uplink module and a secondary identity authentication module;

[0007] The cloud server is used to build a credit data alliance sharing platform, using the affiliated credit terminals as blockchain nodes to collect credit data uploaded by the affiliated credit terminals;

[0008] The data encryption module is used to encrypt the credit data of the user before the affiliated credit terminal transmits the credit data of the user to the credit data alliance sharing platform to generate an encrypted data packet;

[0009] The data decryption module is used to decrypt the encrypted data packet transmitted to the blockchain node, and determine whether to perform data cross-verification based on the decryption result;

[0010] The cross-validation module is used to extract data features from the decrypted data using a pre-built credit feature model, construct a credit feature vector matrix, and perform data cross-validation between the credit feature vector matrix and the credit feature vector matrix stored in each blockchain node in the blockchain network;

[0011] The data uplink module is used to perform a data uplink operation on the credit investigation data processed by the cross-verification module;

[0012] The secondary identity authentication module is used to build a temporary data verification library based on the biometric data stored in each affiliated credit investigation terminal, generate a corresponding biometric data verification scheme based on the reliability level of the credit investigation data, and perform secondary identity authentication on the credit investigation data.

[0013] Furthermore, the cloud server constructs a credit data alliance sharing platform, uses the affiliated credit terminals as blockchain nodes, and the process of collecting credit data uploaded by the affiliated credit terminals includes:

[0014] Using blockchain technology to build a credit data alliance sharing platform, the credit data alliance sharing platform is equipped with a number of blockchain nodes, and each blockchain node is linked to each other to form a blockchain network, and each blockchain node is communicatively linked to a corresponding franchised credit terminal, and the franchised credit terminal is used for users to upload credit data;

[0015] Each participating credit reporting terminal is provided with a local database, which is used to store the user's basic identity data and the user's biometric data, and to generate the user's anonymous identification sequence based on the user's basic identity data, to associate the user's anonymous identification sequence with the user's biometric data, and to publish the user's anonymous identification sequence to the blockchain copies of all blockchain nodes.

[0016] Furthermore, the data encryption module encrypts the credit data before the affiliated credit terminal transmits the user's credit data to the credit data alliance sharing platform. The process of generating an encrypted data packet includes:

[0017] A first key pair and a second key pair are preset between each participating credit reporting terminal and the credit reporting data alliance sharing platform through an asymmetric encryption algorithm, wherein the key pair includes a public key and a private key. When the participating credit reporting terminal transmits the user's credit reporting data to the credit reporting data alliance sharing platform, an anonymous identification sequence associated with the user in the local database is obtained, the anonymous identification sequence and the credit reporting data are preprocessed in data format, the anonymous identification sequence is converted into an anonymous binary string, the credit reporting data is converted into binary data of a fixed length, the anonymous binary string is added to the first segment of the binary data, the binary data after the anonymous binary string is added is encrypted using the first private key to generate encrypted data, and the encrypted data is hashed by applying the SHA-256 hash function to the encrypted data to generate a hash value of the encrypted data, the hash value of the encrypted data is encrypted using the second private key, a digital signature of the encrypted data is generated, the encrypted data and the digital signature are packaged to generate an encrypted data packet, and the encrypted data packet is sent to a blockchain node that is linked to the participating credit reporting terminal.

[0018] Furthermore, the data decryption module decrypts the encrypted data packet transmitted to the blockchain node, and determines whether to perform data cross-verification according to the decryption result, including:

[0019] The blockchain node decrypts the encrypted data in the encrypted data packet by using the first public key to generate decrypted data, and at the same time decrypts the digital signature by using the second public key to obtain a hash value, applies the SHA-256 hash function to the decrypted data to perform a hash operation, obtains the hash value of the decrypted data, and compares the hash value of the decrypted data with the hash value for consistency;

[0020] If the hash value of the decrypted data is consistent with the hash value, the decrypted data is sent to the cross-validation module for data cross-validation;

[0021] If the hash value of the decrypted data is inconsistent with the hash value, the decrypted data will be discarded and a data tampering warning signal will be sent to the participating credit reporting terminal.

[0022] Furthermore, the cross-validation module uses a pre-built credit feature model to extract data features from the decrypted data, constructs a credit feature vector matrix, and cross-validates the credit feature vector matrix with the credit feature vector matrix stored in each blockchain node in the blockchain network. The process includes:

[0023] Pre-constructing a credit feature model, extracting an anonymous binary string corresponding to the first segment of the binary data corresponding to the decrypted data, converting the anonymous binary string into an anonymous identification sequence through data format processing, converting the decrypted data into credit data through data format processing, inputting the credit data into the credit feature model for data feature extraction, and obtaining feature vector data, wherein the feature vector data includes a plurality of feature vector types and feature values ​​corresponding to each feature vector type, obtaining a plurality of feature vector types and feature values ​​corresponding to each feature vector type from the feature vector data, constructing a credit feature vector matrix, and associating the anonymous identification sequence with the credit feature vector matrix;

[0024] Each blockchain node in the blockchain network stores a number of anonymous identification sequences and a credit feature vector matrix associated with each anonymous identification sequence. The anonymous identification sequence is retrieved and matched with a number of anonymous identification sequences stored in each blockchain node in the blockchain network, and the anonymous identification sequence consistent with the anonymous identification sequence and the credit feature vector matrix associated with the anonymous identification sequence in each blockchain node are screened out, and the credit feature vector matrix associated with the anonymous identification sequence in each blockchain node is marked as a credit feature vector matrix to be verified, and the credit feature vector matrices to be verified in each blockchain node are matrix-fused to generate a dense matrix of credit feature vectors to be verified;

[0025] Obtain the reliability level of the credit data according to the credit feature vector matrix and the dense matrix of the credit feature vector to be verified, preset a reliability level threshold, compare the reliability level of the credit data with the reliability level threshold, and if the reliability level of the credit data is greater than or equal to the reliability level threshold, send the credit data to the data uplink module;

[0026] If the reliability level of the credit information data is less than the reliability level threshold, the credit information data and the reliability level of the credit information data are sent to the secondary identity authentication module.

[0027] Furthermore, the process of obtaining the reliability level of the credit data according to the credit feature vector matrix and the dense matrix of the credit feature vector to be verified includes:

[0028] The eigenvalues ​​corresponding to each eigenvector type in the credit feature vector matrix are compared with the eigenvalues ​​corresponding to each eigenvector type of the same type in the dense matrix of the credit feature vector to be verified for feature similarity one by one, and the similarity of each eigenvector type in the credit feature vector matrix is ​​obtained. The similarity of each eigenvector type is used as an evaluation index, and the indicator weight matrix and reliability level of the evaluation index are preset, wherein the weight vector is determined based on the experience of experts, thereby reducing the uncertainty in the fuzzy comprehensive evaluation process, and the membership matrix of the credit data to the reliability level is judged through the fuzzy comprehensive evaluation, and the reliability level of the credit data is obtained according to the membership matrix and the indicator weight matrix.

[0029] Furthermore, the process in which the data uploading module performs a data uploading operation on the credit information data processed by the cross-verification module includes:

[0030] Preset mining nodes, verification rules and consensus mechanism of the blockchain network. The mining node is used to create a new block, package the credit data to be uploaded into the new block, and broadcast the new block to the blockchain network. Other blockchain nodes in the blockchain network verify the new block based on the verification rules and consensus mechanism.

[0031] After the new block is verified, it is added to the end of the blockchain, and the credit information of the new block is updated to the blockchain copies of all blockchain nodes.

[0032] Furthermore, the secondary identity verification module builds a temporary data verification library based on the biometric data stored in each participating credit reporting terminal, generates a corresponding biometric data verification scheme based on the reliability level of the credit reporting data, and the process of performing secondary identity verification on the credit reporting data includes:

[0033] Preset biometric data verification schemes corresponding to different reliability levels. When the reliability level of credit information data is received, obtain the anonymous identification sequence associated with the credit information data, build a temporary data verification library of the anonymous identification sequence, collect the biometric data associated with the anonymous identification sequence stored in the local database of each participating credit information terminal, and store it in the temporary data verification library;

[0034] Generate a corresponding biometric data verification scheme according to the reliability level of the credit data, obtain the biometric data to be verified of the credit data according to the biometric data verification scheme, match the biometric data to be verified with the biometric data in the temporary data verification library, and if the biometric data to be verified matches successfully, send the credit data to the data uplink module;

[0035] If the biometric data to be verified fails to match, the credit data will be eliminated.

[0036] Compared with the prior art, the beneficial effects of the present invention are as follows: constructing a credit data alliance sharing platform, using the affiliated credit terminals as blockchain nodes, collecting the credit data uploaded by the affiliated credit terminals, extracting data features from the decrypted data using a pre-built credit feature model through a cross-validation module, constructing a credit feature vector matrix, and cross-validating the credit feature vector matrix with the credit feature vector matrix stored in each blockchain node in the blockchain network, thereby avoiding the situation where the login account password of the credit data user is stolen or leaked, and false credit data is generated and uploaded to the credit platform, resulting in misleading information in the user's credit record. Since the credit data of different affiliated credit terminals in the blockchain network include but are not limited to multi-dimensional data such as transaction behavior, social behavior, payment behavior, and consumption characteristics, objectively reflecting individual habits, personality, behavior, and preferences, which are relatively stable, through deep mining of credit data, breaking through the traditional thinking of evaluating corporate credit through financial statements, mortgage assets, and guarantee information, integrating behavior, preference, identity, and other data to conduct a more comprehensive assessment of the authenticity of the uploaded credit data, significantly enhancing the accuracy, timeliness, and predictability of the credit data. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is a schematic diagram of a credit data security management system based on cloud services according to an embodiment of the present application. DETAILED DESCRIPTION

[0038] The following is a clear and complete description of the technical solutions in the embodiments of the present application in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present application.

[0039] like Figure 1 As shown, a credit data security management system based on cloud service includes a cloud server, and the cloud server is communicatively connected with a data encryption module, a data decryption module, a cross-verification module, a data uplink module and a secondary identity authentication module;

[0040] The cloud server is used to build a credit data alliance sharing platform, using the affiliated credit terminals as blockchain nodes to collect credit data uploaded by the affiliated credit terminals;

[0041] The data encryption module encrypts the credit data of the user before the affiliated credit terminal transmits the credit data of the user to the credit data alliance sharing platform to generate an encrypted data packet;

[0042] The data decryption module decrypts the encrypted data packet transmitted to the blockchain node, and determines whether to perform data cross-verification based on the decryption result;

[0043] The cross-validation module extracts data features from the decrypted data using a pre-built credit feature model, constructs a credit feature vector matrix, and performs data cross-validation on the credit feature vector matrix and the credit feature vector matrix stored in each blockchain node in the blockchain network;

[0044] The data uploading module performs a data uploading operation on the credit information processed by the cross-verification module;

[0045] The secondary identity authentication module builds a temporary data verification library based on the biometric data stored in each participating credit investigation terminal, generates a corresponding biometric data verification scheme based on the reliability level of the credit investigation data, and performs secondary identity authentication on the credit investigation data.

[0046] It should be further explained that, in the specific implementation process, the cloud server builds a credit data alliance sharing platform, uses the franchised credit terminals as blockchain nodes, and collects the credit data uploaded by the franchised credit terminals, including:

[0047] Using blockchain technology to build a credit data alliance sharing platform, the credit data alliance sharing platform is equipped with a number of blockchain nodes, and each blockchain node is linked to each other to form a blockchain network, and each blockchain node is communicatively linked to a corresponding franchised credit terminal, and the franchised credit terminal is used for users to upload credit data;

[0048] Each participating credit reporting terminal is provided with a local database, which is used to store the user's basic identity data and the user's biometric data, and to generate the user's anonymous identification sequence based on the user's basic identity data, to associate the user's anonymous identification sequence with the user's biometric data, and to publish the user's anonymous identification sequence to the blockchain copies of all blockchain nodes.

[0049] It should be further explained that, in addition to the local database, the franchise credit investigation terminal is also provided with a registration port, a login port and a data entry port. The data entry port is used for users to input credit investigation data, and before the user inputs the credit investigation data to be input, the following steps are also included:

[0050] The user enters basic identity data and the user's biometric data through the registration port, and generates a corresponding login account and login password;

[0051] The user enters the login account and login password through the login port to enter the franchise credit investigation terminal;

[0052] It should be further explained that the user's basic identity data includes personal identity information such as name, gender, age, and ID number. The affiliated credit reporting terminals include various banks, credit reporting agencies, loan institutions, e-commerce platforms, etc.

[0053] It should be further explained that, in the specific implementation process, the data encryption module encrypts the credit data before the affiliated credit terminal transmits the user's credit data to the credit data alliance sharing platform. The process of generating an encrypted data packet includes:

[0054] The first key pair and the second key pair between each participating credit investigation terminal and the credit investigation data alliance sharing platform are preset by an asymmetric encryption algorithm, wherein the key pair includes a public key and a private key. When the participating credit investigation terminal transmits the credit investigation data of the user to the credit investigation data alliance sharing platform, an anonymous identification sequence associated with the user in the local database is obtained, and the anonymous identification sequence and the credit investigation data are preprocessed in data format, the anonymous identification sequence is converted into an anonymous binary string, the credit investigation data is converted into binary data of a fixed length, the anonymous binary string is added to the first segment of the binary data, and the binary data after the anonymous binary string is added is encrypted using the first private key to generate encrypted data. For example, if the asymmetric encryption algorithm used in the present invention is the RSA algorithm, the credit investigation data alliance sharing platform presets the public key and the private key of each participating credit investigation terminal through the RSA algorithm. The first RSA key pair for encryption and the second RSA key pair for digital signature are used to divide the credit data to be transmitted into appropriate data blocks and convert them into binary data. According to the limitations of the RSA algorithm, the length of the data block cannot exceed the length of the public key. Each data block of the credit data is encrypted using the RSA encryption algorithm and the first RSA key to generate a corresponding ciphertext data block. The encrypted ciphertext data blocks are combined together to form complete ciphertext data. At the same time, the SHA-256 hash function is applied to the encrypted data for hash operation to generate a hash value of the encrypted data. The hash value of the encrypted data is encrypted using the second private key to generate a digital signature for the encrypted data. The encrypted data and the digital signature are packaged to generate an encrypted data packet, and the encrypted data packet is sent to the blockchain node that is linked to the credit terminal communication link.

[0055] It should be further explained that, in the specific implementation process, the data decryption module decrypts the encrypted data packet transmitted to the blockchain node, and determines whether to perform data cross-verification according to the decryption result, including:

[0056] The blockchain node decrypts the encrypted data in the encrypted data packet by using the first public key to generate decrypted data, and at the same time decrypts the digital signature by using the second public key to obtain a hash value, applies the SHA-256 hash function to the decrypted data to perform a hash operation, obtains the hash value of the decrypted data, and compares the hash value of the decrypted data with the hash value for consistency;

[0057] If the hash value of the decrypted data is consistent with the hash value, the decrypted data is sent to the cross-validation module for data cross-validation;

[0058] If the hash value of the decrypted data is inconsistent with the hash value, the decrypted data will be discarded and a data tampering warning signal will be sent to the participating credit reporting terminal.

[0059] It should be further explained that, in the specific implementation process, the cross-validation module uses a pre-built credit feature model to extract data features from the decrypted data, constructs a credit feature vector matrix, and cross-validates the credit feature vector matrix with the credit feature vector matrix stored in each blockchain node in the blockchain network. The process includes:

[0060] Pre-constructing a credit feature model, extracting an anonymous binary string corresponding to the first segment of the binary data corresponding to the decrypted data, converting the anonymous binary string into an anonymous identification sequence through data format processing, converting the decrypted data into credit data through data format processing, inputting the credit data into the credit feature model for data feature extraction, and obtaining feature vector data, wherein the feature vector data includes a plurality of feature vector types and feature values ​​corresponding to each feature vector type, obtaining a plurality of feature vector types and feature values ​​corresponding to each feature vector type from the feature vector data, constructing a credit feature vector matrix, and associating the anonymous identification sequence with the credit feature vector matrix;

[0061] Each blockchain node in the blockchain network stores a number of anonymous identification sequences and a credit feature vector matrix associated with each anonymous identification sequence. The anonymous identification sequence is retrieved and matched with a number of anonymous identification sequences stored in each blockchain node in the blockchain network, and the anonymous identification sequence consistent with the anonymous identification sequence and the credit feature vector matrix associated with the anonymous identification sequence in each blockchain node are screened out, and the credit feature vector matrix associated with the anonymous identification sequence in each blockchain node is marked as a credit feature vector matrix to be verified, and the credit feature vector matrices to be verified in each blockchain node are matrix-fused to generate a dense matrix of credit feature vectors to be verified;

[0062] Obtain the reliability level of the credit data according to the credit feature vector matrix and the dense matrix of the credit feature vector to be verified, preset a reliability level threshold, compare the reliability level of the credit data with the reliability level threshold, and if the reliability level of the credit data is greater than or equal to the reliability level threshold, send the credit data to the data uplink module;

[0063] If the reliability level of the credit information data is less than the reliability level threshold, the credit information data and the reliability level of the credit information data are sent to the secondary identity authentication module.

[0064] It should be further explained that, in the specific implementation process, the credit feature vector matrices to be verified in each blockchain node are matrix-fused to generate a dense matrix of credit feature vectors to be verified. Since the types of feature vectors contained in the credit feature vector matrices to be verified in each blockchain node of the blockchain network may be different, the feature vector types contained in the credit feature vector matrices to be verified in one blockchain node may not have corresponding feature vector types in the credit feature vector matrices to be verified in other blockchain nodes. Therefore, the credit feature vector matrices to be verified in each blockchain node are matrix-fused to fill the gaps in the feature vector types in the original credit feature vector matrices to be verified in each blockchain node.

[0065] The specific process of matrix fusion of the credit feature vector matrices to be verified in each blockchain node is as follows: obtaining each eigenvector type contained in the credit feature vector matrix to be verified in each blockchain node, combining each eigenvector type contained in the credit feature vector matrix to be verified in each blockchain node as several eigenvector types included in the dense matrix of the credit feature vector to be verified, then obtaining the eigenvalues ​​corresponding to each eigenvector type contained in the credit feature vector matrix to be verified in each blockchain node, performing data mean operation on the eigenvalues ​​corresponding to each eigenvector type contained in the credit feature vector matrix to be verified in all blockchain nodes, obtaining the mean eigenvalue corresponding to each eigenvector type, using the mean eigenvalue corresponding to each eigenvector type as the eigenvalue of each eigenvector type corresponding to the dense matrix of the credit feature vector to be verified, and completing the construction of the dense matrix of the credit feature vector to be verified.

[0066] In the present invention, the feature vector data of the credit investigation data includes various feature values ​​reflecting the user's credit status, personal information, behavioral data, and financial data. The feature values ​​corresponding to these different feature vector types are used to describe the user's credit status and credit risk. The feature vector types and feature values ​​in the credit investigation data feature vector of the present invention include but are not limited to:

[0067] Financial information characteristics: income level, debt amount, loan amount, credit card usage amount and other financial information, asset status, real estate and vehicle information and other asset and liability information;

[0068] Credit history characteristics: credit history information such as repayment records, overdue status, and credit card bill repayment records;

[0069] Credit card related information such as credit limit, line of credit, credit limit usage, etc.

[0070] Inquiry record characteristics: credit inquiry records, credit inquiry times, and other inquiry information related to credit review;

[0071] Behavioral data characteristics: personal behavior data such as consumption amount records, loan amount records, investment and financial management records, etc.

[0072] It should be further explained that, in the specific implementation process, the process of obtaining the reliability level of the credit data according to the credit feature vector matrix and the dense matrix of the credit feature vector to be verified includes:

[0073] The eigenvalues ​​corresponding to each eigenvector type in the credit feature vector matrix are compared with the eigenvalues ​​corresponding to each eigenvector type of the same type in the dense matrix of the credit feature vector to be verified for feature similarity one by one, and the similarity of each eigenvector type in the credit feature vector matrix is ​​obtained. The similarity of each eigenvector type is used as an evaluation index, and the indicator weight matrix and reliability level of the evaluation index are preset, wherein the weight vector is determined based on the experience of experts, thereby reducing the uncertainty in the fuzzy comprehensive evaluation process, and the membership matrix of the credit data to the reliability level is judged through the fuzzy comprehensive evaluation, and the reliability level of the credit data is obtained according to the membership matrix and the indicator weight matrix.

[0074] It should be further explained that, in the specific implementation process, the formula for obtaining the similarity of each feature vector type is:

[0075]

[0076] Among them, D ai represents the eigenvalue of the i-th eigenvector type in the credit feature vector matrix, D bi represents the eigenvalue of the i-th eigenvector type in the dense matrix of the credit feature vector to be verified, F i Represents the similarity of the i-th type of eigenvector in the credit feature vector matrix;

[0077] It should be further explained that, in the specific implementation process, the process of obtaining the reliability level of credit data according to the membership matrix and the indicator weight matrix includes:

[0078] The indicator weights and the membership matrix of the evaluation indicators are integrated through the following formula to obtain a fuzzy comprehensive evaluation matrix of the evaluation indicators, and the membership levels of the credit data corresponding to different reliability levels are obtained according to the fuzzy comprehensive evaluation matrix;

[0079] Wherein, the formula is:

[0080] M = αM1 × βM2;

[0081] Among them, M is the fuzzy comprehensive evaluation matrix of the evaluation index, M1 is the weight matrix of the indicator weights of the evaluation index, M2 is the membership matrix, "×" represents the addition of the elements at corresponding positions of the weight matrix of the evaluation index and the membership matrix, and α and β are weighting parameters for controlling the balance between the weight matrix and the membership matrix in the fuzzy comprehensive evaluation matrix of the evaluation index.

[0082] It should be further explained that, in the specific implementation process, the process in which the data chain module performs a data chain operation on the credit information data processed by the cross-verification module includes:

[0083] Preset mining nodes, verification rules and consensus mechanism of the blockchain network. The mining node is used to create a new block, package the credit data to be uploaded into the new block, and broadcast the new block to the blockchain network. Other blockchain nodes in the blockchain network verify the new block based on the verification rules and consensus mechanism.

[0084] After the new block is verified, it is added to the end of the blockchain, and the credit information of the new block is updated to the blockchain copies of all blockchain nodes.

[0085] It should be further explained that, in the specific implementation process, the secondary identity authentication module builds a temporary data verification library based on the biometric data stored in each participating credit reporting terminal, generates a corresponding biometric data verification scheme based on the reliability level of the credit reporting data, and the process of performing secondary identity authentication on the credit reporting data includes:

[0086] Preset biometric data verification schemes corresponding to different reliability levels. When the reliability level of credit information data is received, obtain the anonymous identification sequence associated with the credit information data, build a temporary data verification library of the anonymous identification sequence, collect the biometric data associated with the anonymous identification sequence stored in the local database of each participating credit information terminal, and store it in the temporary data verification library;

[0087] Generate a corresponding biometric data verification scheme according to the reliability level of the credit data, obtain the biometric data to be verified of the credit data according to the biometric data verification scheme, match the biometric data to be verified with the biometric data in the temporary data verification library, and if the biometric data to be verified matches successfully, send the credit data to the data uplink module;

[0088] If the biometric data to be verified fails to match, the credit data will be eliminated.

[0089] It should be further explained that, in the specific implementation process, biometric data includes but is not limited to: fingerprint data, face data, iris data, DNA data, etc. The biometric data verification scheme sets different types of biometric data for verification according to the reliability level. Specifically, for example, the lower the reliability level, the worse the reliability. The feature value range in the credit data does not conform to the feature value range in the historical credit data already in the blockchain network. The reliability level is set to 1. The biometric data types that need to be verified in the biometric data verification scheme corresponding to reliability level 1 include fingerprint data, face data, iris data, and DNA data. The reliability level is set to 2. The biometric data types that need to be verified in the biometric data verification scheme corresponding to reliability level 2 include fingerprint data, face data, and iris data. The reliability level is set to 3. The biometric data types that need to be verified in the biometric data verification scheme corresponding to reliability level 3 include fingerprint data, face data, and iris data. The types of biometric data that need to be verified in the data verification scheme include fingerprint data and face data. The reliability level is set to 4. The types of biometric data that need to be verified in the biometric data verification scheme corresponding to reliability level 4 include fingerprint data, face data, etc. When the secondary identity authentication module generates the biometric data verification scheme, it is sent to the affiliated credit reporting terminal. The affiliated credit reporting terminal sends the type of biometric data that needs to be verified to the user according to the biometric data verification scheme. The user uploads the corresponding biometric data to be verified according to the biometric data type that needs to be verified, and matches the biometric data to be verified with the biometric data in the temporary data verification library. If the biometric data to be verified matches successfully, it indicates that the credit data is uploaded by the user himself, and the credit data is sent to the data uplink module. If the biometric data to be verified fails to match successfully, it indicates that the credit data is false data and the credit data is eliminated.

[0090] The rapid development of the Internet has made all network data a data source for credit evaluation, greatly enriching the information source channels of traditional credit investigation. The data used in Internet credit investigation not only includes traditional banking credit records, consumption records, and payment flows, but also includes identity, social, business, and daily activities and behavioral preference data, behavioral characteristic data, etc. The use of big data technology to integrate more Internet credit information of users at various affiliated credit investigation terminals into the credit investigation data alliance sharing platform can not only effectively identify risks, but also predict trends. Therefore, in today's era of information explosion, it is difficult to meet the credit assessment needs by relying solely on the data of the People's Bank of China's credit investigation system. The present invention converts the original massive and messy information into usable credit data through data cleaning, effective classification, data merging, and deep mining, and plays an important role in enhancing the accuracy, timeliness, and predictability of credit investigation data.

[0091] The above embodiments are only used to illustrate the technical method of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical method of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present invention.

Claims

1. A credit data security management system based on cloud services, comprising a cloud server, characterized in that: The cloud server is communicatively connected with a data encryption module, a data decryption module, a cross-verification module, a data uplink module and a secondary identity authentication module; The cloud server is used to build a credit data alliance sharing platform, using the affiliated credit terminals as blockchain nodes to collect credit data uploaded by the affiliated credit terminals; The data encryption module is used to encrypt the credit data of the user before the affiliated credit terminal transmits the credit data of the user to the credit data alliance sharing platform to generate an encrypted data packet; The data decryption module is used to decrypt the encrypted data packet transmitted to the blockchain node, and determine whether to perform data cross-verification based on the decryption result; The cross-validation module is used to extract data features from the decrypted data using a pre-built credit feature model, construct a credit feature vector matrix, and perform data cross-validation between the credit feature vector matrix and the credit feature vector matrix stored in each blockchain node in the blockchain network; The data uplink module is used to perform a data uplink operation on the credit investigation data processed by the cross-verification module; The secondary identity authentication module is used to build a temporary data verification library based on the biometric data stored in each affiliated credit investigation terminal, generate a corresponding biometric data verification scheme based on the reliability level of the credit investigation data, and perform secondary identity authentication on the credit investigation data.

2. The credit information data security management system based on cloud service according to claim 1, characterized in that: The cloud server constructs a credit data alliance sharing platform, uses the affiliated credit terminals as blockchain nodes, and collects the credit data uploaded by the affiliated credit terminals, including: Using blockchain technology to build a credit data alliance sharing platform, the credit data alliance sharing platform is equipped with a number of blockchain nodes, and each blockchain node is linked to each other to form a blockchain network, and each blockchain node is communicatively linked to a corresponding franchised credit terminal, and the franchised credit terminal is used for users to upload credit data; Each participating credit reporting terminal is provided with a local database, which is used to store the user's basic identity data and the user's biometric data, and to generate the user's anonymous identification sequence based on the user's basic identity data, to associate the user's anonymous identification sequence with the user's biometric data, and to publish the user's anonymous identification sequence to the blockchain copies of all blockchain nodes.

3. The credit information data security management system based on cloud service according to claim 2, characterized in that: The data encryption module encrypts the credit data before the affiliated credit terminal transmits the user's credit data to the credit data alliance sharing platform. The process of generating an encrypted data packet includes: A first key pair and a second key pair are preset between each participating credit reporting terminal and the credit reporting data alliance sharing platform through an asymmetric encryption algorithm, wherein the key pair includes a public key and a private key. When the participating credit reporting terminal transmits the user's credit reporting data to the credit reporting data alliance sharing platform, an anonymous identification sequence associated with the user in the local database is obtained, the anonymous identification sequence and the credit reporting data are preprocessed in data format, the anonymous identification sequence is converted into an anonymous binary string, the credit reporting data is converted into binary data of a fixed length, the anonymous binary string is added to the first segment of the binary data, the binary data after the anonymous binary string is added is encrypted using the first private key to generate encrypted data, and the encrypted data is hashed by applying the SHA-256 hash function to the encrypted data to generate a hash value of the encrypted data, the hash value of the encrypted data is encrypted using the second private key, a digital signature of the encrypted data is generated, the encrypted data and the digital signature are packaged to generate an encrypted data packet, and the encrypted data packet is sent to a blockchain node that is linked to the participating credit reporting terminal.

4. The credit information data security management system based on cloud service according to claim 3, characterized in that: The data decryption module decrypts the encrypted data packet transmitted to the blockchain node, and determines whether to perform data cross-verification according to the decryption result. The process includes: The blockchain node decrypts the encrypted data in the encrypted data packet by using the first public key to generate decrypted data, and at the same time decrypts the digital signature by using the second public key to obtain a hash value, applies the SHA-256 hash function to the decrypted data to perform a hash operation, obtains the hash value of the decrypted data, and compares the hash value of the decrypted data with the hash value for consistency; If the hash value of the decrypted data is consistent with the hash value, the decrypted data is sent to the cross-validation module for data cross-validation; If the hash value of the decrypted data is inconsistent with the hash value, the decrypted data will be discarded and a data tampering warning signal will be sent to the participating credit reporting terminal.

5. The credit information data security management system based on cloud service according to claim 4, characterized in that: The cross-validation module extracts data features from the decrypted data using a pre-built credit feature model, constructs a credit feature vector matrix, and cross-validates the credit feature vector matrix with the credit feature vector matrix stored in each blockchain node in the blockchain network. The process includes: Pre-constructing a credit feature model, extracting an anonymous binary string corresponding to the first segment of the binary data corresponding to the decrypted data, converting the anonymous binary string into an anonymous identification sequence through data format processing, converting the decrypted data into credit data through data format processing, inputting the credit data into the credit feature model for data feature extraction, and obtaining feature vector data, wherein the feature vector data includes a plurality of feature vector types and feature values ​​corresponding to each feature vector type, obtaining a plurality of feature vector types and feature values ​​corresponding to each feature vector type from the feature vector data, constructing a credit feature vector matrix, and associating the anonymous identification sequence with the credit feature vector matrix; Each blockchain node in the blockchain network stores a number of anonymous identification sequences and a credit feature vector matrix associated with each anonymous identification sequence. The anonymous identification sequence is retrieved and matched with a number of anonymous identification sequences stored in each blockchain node in the blockchain network, and the anonymous identification sequence consistent with the anonymous identification sequence and the credit feature vector matrix associated with the anonymous identification sequence in each blockchain node are screened out, and the credit feature vector matrix associated with the anonymous identification sequence in each blockchain node is marked as a credit feature vector matrix to be verified, and the credit feature vector matrices to be verified in each blockchain node are matrix-fused to generate a dense matrix of credit feature vectors to be verified; Obtain the reliability level of the credit data according to the credit feature vector matrix and the dense matrix of the credit feature vector to be verified, preset a reliability level threshold, compare the reliability level of the credit data with the reliability level threshold, and if the reliability level of the credit data is greater than or equal to the reliability level threshold, send the credit data to the data uplink module; If the reliability level of the credit information data is less than the reliability level threshold, the credit information data and the reliability level of the credit information data are sent to the secondary identity authentication module.

6. The credit information data security management system based on cloud service according to claim 5, characterized in that: The process of obtaining the reliability level of credit data according to the credit feature vector matrix and the dense matrix of credit feature vectors to be verified includes: The eigenvalues ​​corresponding to each eigenvector type in the credit feature vector matrix are compared one by one with the eigenvalues ​​corresponding to each eigenvector type of the same type in the dense matrix of the credit feature vector to be verified, and the similarity of each eigenvector type in the credit feature vector matrix is ​​obtained. The similarity of each eigenvector type is used as an evaluation index, and the indicator weight matrix and reliability level of the evaluation index are preset. The membership matrix of the credit data to the reliability level is judged through fuzzy comprehensive evaluation, and the reliability level of the credit data is obtained according to the membership matrix and the indicator weight matrix.

7. The credit information data security management system based on cloud service according to claim 6, characterized in that: The process of the data chain module performing a data chain operation on the credit information data processed by the cross-verification module includes: Preset mining nodes, verification rules and consensus mechanism of the blockchain network. The mining node is used to create a new block, package the credit data to be uploaded into the new block, and broadcast the new block to the blockchain network. Other blockchain nodes in the blockchain network verify the new block based on the verification rules and consensus mechanism. After the new block is verified, it is added to the end of the blockchain, and the credit information of the new block is updated to the blockchain copies of all blockchain nodes.

8. The credit information data security management system based on cloud service according to claim 7, characterized in that: The secondary identity authentication module builds a temporary data verification library based on the biometric data stored in each participating credit investigation terminal, and generates a corresponding biometric data verification scheme based on the reliability level of the credit investigation data. The process of performing secondary identity authentication on the credit investigation data includes: Preset biometric data verification schemes corresponding to different reliability levels. When the reliability level of credit information data is received, obtain the anonymous identification sequence associated with the credit information data, build a temporary data verification library of the anonymous identification sequence, collect the biometric data associated with the anonymous identification sequence stored in the local database of each participating credit information terminal, and store it in the temporary data verification library; Generate a corresponding biometric data verification scheme according to the reliability level of the credit data, obtain the biometric data to be verified of the credit data according to the biometric data verification scheme, match the biometric data to be verified with the biometric data in the temporary data verification library, and if the biometric data to be verified matches successfully, send the credit data to the data uplink module; If the biometric data to be verified fails to match, the credit data will be eliminated.

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