E-government privacy security protection system based on blockchain technology

Abstract

The present application relates to the field of e-government technology, and discloses an e-government privacy and security protection system based on blockchain technology, including a blockchain layer, a data storage layer, a smart contract layer, a user layer, a privacy computing module, a dynamic authorization and trust scoring module, a revocable access permission module, a data tampering detection and reporting module, a decentralized identity management module, and an on-chain audit module; the blockchain layer is used to record and store data and transactions generated in the e-government process; the data storage layer is used to store encrypted sensitive data, and store the corresponding hash value and index on the blockchain. The computing task allocation is dynamically adjusted through the automatic coordination mechanism of the smart contract to ensure computing efficiency and data privacy. At the same time, the on-chain audit tool immediately verifies the compliance of the results and generates reliable decision-making recommendations, thereby providing reliable decision support and avoiding data leakage risks.

Description

Technical Field

[0001] The present invention relates to the field of e-government technology, and specifically to an e-government privacy security protection system based on blockchain technology. Background Art

[0002] E-government refers to the process of providing government services, information and management in an electronic way by using information technology, especially Internet technology. It aims to improve the efficiency and transparency of government services and facilitate the interaction between the public and enterprises and government departments. However, with the widespread application of e-government, the amount of personal and corporate data involved has increased significantly, and the issue of data privacy protection has become increasingly prominent. Sensitive information processed in e-government systems, such as personal identity information, financial records, and medical data, may lead to privacy leaks and security risks if not adequately protected. In response to these challenges, an e-government privacy security system has emerged. The system protects the privacy data involved in the e-government process through advanced information security technologies such as encryption, permission management and data tampering detection. In addition, in recent years, blockchain technology has become an effective means to enhance the privacy security of e-government due to its decentralized, tamper-proof and highly transparent characteristics.

[0003] First, in the existing e-government system, when multiple departments collaborate to process sensitive data, they usually face challenges in data privacy and compliance checks. Traditional methods rely on centralized computing and auditing, which can easily lead to data leakage and opaque operations. In addition, existing audit systems often operate independently, disconnected from the data processing process, and unable to verify and ensure data compliance in real time. With the increasingly stringent data privacy protection regulations, how to achieve efficient compliance audits while ensuring data privacy has become an urgent problem to be solved. Although blockchain technology provides data immutability, relying solely on blockchain cannot meet privacy computing and auditing needs;

[0004] Secondly, in the existing e-government system, permission management is usually static and difficult to adjust according to the real-time changes in user behavior. This static permission management model cannot respond to abnormal user behavior in a timely manner and easily leads to the leakage of sensitive data. Although the traditional trust scoring system can score based on the user's historical behavior, these scores are often not closely integrated with permission management, resulting in the system being unable to adjust permissions based on real-time scores, increasing security risks. At the same time, the existing permission revocation mechanism mostly relies on manual operations or preset rules, lacking flexibility and response speed;

[0005] Furthermore, existing e-government systems usually rely on centralized authentication methods. Although they can provide a certain degree of security, they lack flexibility and scalability when dealing with complex scenarios. The demand for multi-level identity authentication is increasing, especially in e-government systems, where different operations may require different levels of identity authentication. However, traditional systems are difficult to flexibly configure and dynamically adjust the strength of identity authentication, and there are security risks when identity information is transferred or inherited. In addition, existing dynamic authorization systems often fail to fully consider the differences in the levels of identity authentication, resulting in inaccurate authorization.

[0006] Finally, in e-government systems, data tampering detection and auditing are the key to ensuring data security and integrity. However, existing tampering detection methods are often limited to data verification within a single chain and lack effective detection of cross-chain data consistency, which can easily lead to data tampering in cross-chain operations not being discovered in a timely manner. In addition, most of the existing audit systems are post-audits, which make it difficult to respond to tampering behavior in a timely manner, affecting the accuracy and timeliness of management decisions. Summary of the invention

[0007] In view of the shortcomings of the existing technology, the present invention provides an e-government privacy and security protection system based on blockchain technology, which solves the problem of being unable to verify and ensure the compliance of data in real time.

[0008] To achieve the above objectives, the present invention is implemented through the following technical solutions: an e-government privacy and security protection system based on blockchain technology, including a blockchain layer, a data storage layer, a smart contract layer, a user layer, a privacy computing module, a dynamic authorization and trust scoring module, a revocable access permission module, a data tampering detection and reporting module, a decentralized identity management module and an on-chain audit module;

[0009] The blockchain layer is used to record and store data and transactions generated in the e-government process;

[0010] The data storage layer is used to store encrypted sensitive data and store corresponding hash values ​​and indexes on the blockchain;

[0011] The smart contract layer is used to define and execute rules and processes related to e-government;

[0012] The user layer is used to interact with the system through decentralized applications;

[0013] The privacy computing module is used to protect data privacy when multiple departments collaborate to process data, and the privacy computing module includes a computing capability evaluation unit and a computing strategy adjustment unit;

[0014] The computing capacity evaluation unit is used to evaluate the computing resources and data characteristics of each participant;

[0015] The computing strategy adjustment unit dynamically adjusts the computing task allocation based on the evaluation result, and the adjustment process includes the following steps:

[0016] S1. Establish a computing capability model, the parameters of which include: computing resource quantity RR, task execution time TT, and data characteristic coefficient DD;

[0017] S2. Calculation strategy optimization, calculate the optimal allocation plan through the following formula:

[0018]

[0019] Among them, C opt Represents the optimized computing power, R i represents the computing resources of the ith participant, W i Indicates the calculation weight, T i Indicates the task execution time;

[0020] The dynamic authorization and trust scoring module is used to evaluate the user's credibility in real time based on the user's behavior and on-chain records, and dynamically adjust the user's permissions;

[0021] The revocable access permission module is used to dynamically adjust the revocation conditions of permissions according to user behavior patterns;

[0022] The data tampering detection and reporting module is used to monitor and report data tampering behavior;

[0023] The decentralized identity management module is used to support multi-level verification and management of user identities;

[0024] The on-chain audit module is used to perform complex compliance checks while protecting data privacy.

[0025] Preferably, the blockchain layer includes a transaction record module, a timestamp module, a data verification module and a consensus algorithm module;

[0026] The transaction record module is used to record all transactions generated by user operations;

[0027] The timestamp module is used to generate a timestamp for each transaction;

[0028] The data verification module is used to verify data integrity through a hash algorithm;

[0029] The consensus algorithm module is used to reach a consensus in the blockchain network through a consensus algorithm, and the consensus algorithm includes PoW, PoS and other consensus mechanisms applicable to the alliance chain.

[0030] Preferably, the data storage layer includes an encryption module, a decryption module and a hash generation module;

[0031] The encryption module is used to encrypt sensitive data, and the encryption module adopts an asymmetric encryption algorithm;

[0032] The decryption module is used to decrypt stored data for authorized users;

[0033] The hash generation module is used to generate and store the hash value of the data.

[0034] Preferably, the smart contract layer includes a contract execution module and a rule management module;

[0035] The contract execution module is used to execute a predefined smart contract;

[0036] The rule management module is used to manage and update the rules in the smart contract. The rules in the smart contract include but are not limited to defining new rules, updating existing rules, rule review and verification, and rule archiving and version control.

[0037] Preferably, the user layer includes an identity authentication module, an interactive interface mode and a data query module;

[0038] The identity authentication module is used to perform user identity authentication;

[0039] The interactive interface module is used to provide an interactive interface between the user and the smart contract and data storage layer;

[0040] The data query module is used to query the data that the user has permission to access.

[0041] Preferably, the dynamic authorization and trust scoring module includes a behavior recording unit and a scoring calculation unit;

[0042] The behavior recording unit is used to record the user's interactive behavior and transaction history;

[0043] The score calculation unit is used to calculate the user's trust score, and the calculation formula is:

[0044] S trust =α×R interactions +β×T history +γ×F feedback

[0045] Among them, S trust represents the trust score, R interactions represents the user's interactive behavior (such as the number of interactions between the user and the system, the complexity of the operation), T history represents the user's transaction history (such as the number of transactions and success rate of the user in the past), F feedback represents community feedback (such as other users' evaluation of the user and the trust report generated by the system), and α, β and γ are the weight coefficients of each factor.

[0046] Preferably, the revocable access rights module includes a behavior analysis unit and a dynamic adjustment unit;

[0047] The behavior analysis unit is used to collect and analyze the user's operation behavior, establish a behavior pattern model, and establish parameters including: user operation frequency F, operation complexity C, historical trust score S trust , the establishment steps are as follows:

[0048] S1. Data collection: Collect all user operations in the system, including but not limited to login, data access, permission application, operation frequency, collect contextual information related to user behavior, such as time, location, and device information, and finally obtain the user's operation history in the past period of time to establish a baseline behavior pattern;

[0049] S2. Data preprocessing: Remove invalid data, abnormal data, and noise data to ensure the accuracy of the analysis, and then convert different types of behavioral data into a unified standard form, such as standardizing time data into a unified time unit and standardizing operation frequency into the number of operations / unit time;

[0050] S3, Feature extraction: Extract key behavior features from the collected data, such as operation frequency F, operation complexity C, average response time R, amount of sensitive data accessed D, and extract contextual features related to user behavior, such as operation time period and device type used;

[0051] S4. Behavior pattern modeling: Based on the extracted behavior features, a clustering algorithm is used to cluster the user behavior data to identify different behavior pattern types, and then a random forest algorithm is selected for modeling. The selected model is trained using historical behavior data to obtain a preliminary behavior pattern model.

[0052] S6. Model verification and adjustment: Use the test data set to verify the accuracy and reliability of the model, calculate the model's accuracy, recall rate, and F1 value performance indicators, and optimize and adjust the model based on the verification results, which may include feature selection and parameter adjustment;

[0053] S7. Model deployment and real-time update: Deploy the verified behavior pattern model to the behavior analysis unit for real-time analysis of user operation behavior. Update the model regularly or in real time to adapt to changes in user behavior. Keep the model up-to-date and accurate through incremental learning or online learning mechanisms.

[0054] The dynamic adjustment unit dynamically adjusts the permission revocation conditions based on the following algorithm:

[0055] P revoke =f(S trust ,B)

[0056] Among them, P revoke The probability S of revoking permission trust is the current trust score, and B is the user's behavior pattern.

[0057] Preferably, the data tampering detection and reporting module includes a data monitoring unit and a cross-validation unit;

[0058] The data monitoring unit is used to monitor the data status on and off the chain in real time;

[0059] The cross-validation unit, the cross-validation process comprises the following steps:

[0060] S1. Obtain data hash values ​​of different chains;

[0061] S2. Perform cross validation using the following formula:

[0062] V cross =H(D chain1 )⊕H(D chain2 )

[0063] Where V cross represents the cross validation result, H(D chain1 ) and H(D chain2 ) represent the data hash values ​​of chain 1 and chain 2 respectively;

[0064] If V cross =0, the data is consistent; otherwise, a data tampering report is generated.

[0065] Preferably, the decentralized identity management module includes a basic verification unit, an advanced verification unit, and an inheritance and transfer unit;

[0066] The basic authentication unit is used to perform basic identity authentication, which includes but is not limited to password and biometrics;

[0067] The advanced authentication unit is used to perform advanced identity authentication, which includes but is not limited to multi-factor authentication;

[0068] The inheritance and transfer unit is used to support the safe transfer and inheritance of identity information.

[0069] Preferably, the on-chain audit module includes a compliance check unit, an audit log unit and a decision support unit;

[0070] The compliance checking unit is used to check the on-chain data according to predefined compliance rules;

[0071] The audit log unit is used to generate an audit log and store it on the blockchain for traceability;

[0072] The decision support unit generates management suggestions according to the following formula:

[0073]

[0074] Among them, D support To support decision making, i A is the weight of each audit result. i The corresponding audit results.

[0075] The present invention provides an e-government privacy and security protection system based on blockchain technology.

[0076] Beneficial effects:

[0077] 1. The present invention dynamically adjusts the allocation of computing tasks through the automatic coordination mechanism of smart contracts to ensure computing efficiency and data privacy. After the calculation is completed, the on-chain audit tool immediately verifies the compliance of the results and generates reliable decision-making recommendations. This combination effectively realizes the seamless connection between privacy protection and compliance auditing, and provides reliable decision-making support in an environment with high privacy requirements while avoiding the risk of data leakage.

[0078] 2. The dynamic authorization and trust scoring module of the present invention evaluates user behavior in real time and adjusts the permission level, while the adaptive trust model dynamically adjusts the permission revocation conditions according to the changes in the trust score. When the trust score decreases, the system can immediately strengthen permission management and revoke sensitive permissions in advance. This combination improves the security and responsiveness of the system and effectively responds to abnormal behavior.

[0079] 3. The decentralized identity management module of the present invention provides multi-level identity authentication and identity information transfer. The verification results directly affect the score in the trust scoring system, thereby affecting the granting of permissions. High-level verification improves the trust score and permission level, while low-level or abnormal behavior automatically reduces permissions. This combination ensures flexible and secure permission management and prevents identity abuse.

[0080] 4. Accurately detect tampering through cross-chain data cross-validation, and the results are directly fed back to the on-chain audit tool for further verification and compliance reports. The decision support system uses these reports to provide reliable management recommendations. This combination enhances the system's tampering detection capabilities and response efficiency, and improves overall security and management levels. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] Figure 1 It is a system module distribution diagram of the present invention;

[0082] Figure 2 This is a schematic diagram of the privacy computing module of the present invention;

[0083] Figure 3This is a schematic diagram of the blockchain layer of the present invention;

[0084] Figure 4 It is a schematic diagram of the data storage layer of the present invention;

[0085] Figure 5 It is a schematic diagram of the smart contract layer of the present invention;

[0086] Figure 6 A schematic diagram of the user layer of the present invention;

[0087] Figure 7 This is a schematic diagram of the dynamic authorization and trust scoring module of the present invention;

[0088] Figure 8 A schematic diagram of a revocable access permission module of the present invention;

[0089] Fig. 9 This is a schematic diagram of a data tampering detection and reporting module of the present invention;

[0090] Fig.10 This is a schematic diagram of a decentralized identity management module of the present invention;

[0091] Fig.11 Schematic diagram of the on-chain audit module of the present invention. DETAILED DESCRIPTION

[0092] The following will be combined with the drawings in the specification 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.

[0093] Example:

[0094] Please refer to the attached Figure 1 -Attached Figure 2 , an embodiment of the present invention provides an e-government privacy and security protection system based on blockchain technology, including a blockchain layer, a data storage layer, a smart contract layer, a user layer, a privacy computing module, a dynamic authorization and trust scoring module, a revocable access rights module, a data tampering detection and reporting module, a decentralized identity management module and an on-chain audit module;

[0095] The blockchain layer is used to record and store data and transactions generated in the e-government process;

[0096] The data storage layer is used to store encrypted sensitive data and store corresponding hash values ​​and indexes on the blockchain;

[0097] The smart contract layer is used to define and execute rules and processes related to e-government;

[0098] The user layer is used to interact with the system through decentralized applications;

[0099] The privacy computing module is used to protect data privacy when multiple departments collaborate to process data, and the privacy computing module includes a computing capability evaluation unit and a computing strategy adjustment unit;

[0100] The computing capacity evaluation unit is used to evaluate the computing resources and data characteristics of each participant;

[0101] The computing strategy adjustment unit dynamically adjusts the computing task allocation based on the evaluation result, and the adjustment process includes the following steps:

[0102] S1. Establish a computing capability model, the parameters of which include: computing resource quantity RR, task execution time TT, and data characteristic coefficient DD;

[0103] S2. Calculation strategy optimization, calculate the optimal allocation plan through the following formula:

[0104]

[0105] Among them, C opt Represents the optimized computing power, R i represents the computing resources of the ith participant, W i Indicates the calculation weight, T i Indicates the task execution time;

[0106] The dynamic authorization and trust scoring module is used to evaluate the user's credibility in real time based on the user's behavior and on-chain records, and dynamically adjust the user's permissions;

[0107] The revocable access permission module is used to dynamically adjust the revocation conditions of permissions according to user behavior patterns;

[0108] The data tampering detection and reporting module is used to monitor and report data tampering behavior;

[0109] The decentralized identity management module is used to support multi-level verification and management of user identities;

[0110] The on-chain audit module is used to perform complex compliance checks while protecting data privacy.

[0111] Specifically, the privacy and security protection of e-government data is achieved through the collaborative work of the blockchain layer, data storage layer, smart contract layer, user layer, privacy computing module, dynamic authorization and trust scoring module, revocable access permission module, data tampering detection and reporting module, decentralized identity management module and on-chain audit module. During the operation of the system, users interact with the user layer through decentralized applications to submit e-government requests; the user layer interacts with the smart contract layer, and the smart contract layer defines and executes the corresponding rules and processes. At this time, the privacy computing module is started. First, the computing power evaluation unit evaluates the computing resources of each participant, such as CPU, memory, and data size and complexity, and dynamically adjusts the allocation of computing tasks based on these evaluation results in the computing strategy adjustment unit, and optimizes the computing strategy to maximize computing power and efficiency. After the calculation is completed, the data storage layer is responsible for encrypting and storing the processing results, and storing the corresponding hash value or index on the blockchain to ensure data privacy. The dynamic authorization and trust scoring module evaluates the credibility of users by real-time monitoring their behaviors and records on the chain, and dynamically adjusts their permissions. For example, when the trust score is reduced, the revocable access permission module dynamically adjusts or revokes their access permissions to certain sensitive data according to their behavior patterns. During the whole process, the data tampering detection and reporting module continuously monitors the data status on and off the chain. Once data tampering is detected, a report is immediately generated and an alarm is sent. The decentralized identity management module ensures the reliability and security of user identities through a multi-level identity authentication mechanism, and supports the secure transfer and inheritance of identity information. Finally, the on-chain audit module performs complex compliance checks on the entire process under the premise of protecting data privacy, generates audit logs and stores them on the blockchain for future traceability and management. Data exchange and control instructions are transmitted between modules through the smart contract layer to achieve safe and efficient operation of the e-government system.

[0112] Please see attached Figure 3 -Attached Figure 5 , the blockchain layer includes a transaction record module, a timestamp module, a data verification module and a consensus algorithm module;

[0113] The transaction record module is used to record all transactions generated by user operations;

[0114] The timestamp module is used to generate a timestamp for each transaction;

[0115] The data verification module is used to verify data integrity through a hash algorithm;

[0116] The consensus algorithm module is used to reach a consensus in the blockchain network through a consensus algorithm, and the consensus algorithm includes PoW, PoS and other consensus mechanisms applicable to the alliance chain;

[0117] The data storage layer includes an encryption module, a decryption module and a hash generation module;

[0118] The encryption module is used to encrypt sensitive data, and the encryption module adopts an asymmetric encryption algorithm;

[0119] The decryption module is used to decrypt stored data for authorized users;

[0120] The hash generation module is used to generate and store the hash value of the data;

[0121] The smart contract layer includes a contract execution module and a rule management module;

[0122] The contract execution module is used to execute a predefined smart contract;

[0123] The rule management module is used to manage and update the rules in the smart contract. The rules in the smart contract include but are not limited to defining new rules, updating existing rules, rule review and verification, and rule archiving and version control.

[0124] Specifically, when a user operates in the system, the transaction recording module first records the transaction data generated by all user operations, and generates an accurate timestamp for each transaction through the timestamp module to ensure the timing and traceability of the transaction. The generated transaction data and timestamp are then passed through the data verification module, using the hash algorithm to verify the data integrity and generate a unique hash value to prevent data tampering. The verified data is passed through the consensus algorithm module in the blockchain network through PoW, PoS or other consensus mechanisms suitable for the alliance chain to reach a network consensus, and the transaction is recorded on the blockchain. Subsequently, the encryption module in the data storage layer performs asymmetric encryption on the transaction data and stores the encrypted data in the system's secure storage space. At the same time, the hash generation module generates the corresponding data hash value and stores it in the block On the chain, the integrity and verifiability of the data are ensured. When authorized users need to access stored data, they can obtain data through the decryption module. The decryption process is limited to authorized users to ensure the security of data access. The smart contract layer executes predefined smart contracts through the contract execution module, and processes data and user requests according to the contract rules managed and updated in the rule management module. These rules include but are not limited to defining new rules, updating existing rules, rule review and verification, and archiving and version control of rules. In addition, when system rules change, the automatic upgrade module automatically upgrades smart contracts to adapt to the new rules and ensures the consistency and validity of all contracts in the blockchain network. Through the orderly collaboration of various module units, the system can ensure the privacy, security and integrity of e-government data during operation, storage, verification, encryption and contract execution.

[0125] Please refer to the attached Figure 6 -Attached Figure 8 , the user layer includes an identity authentication module, an interactive interface mode and a data query module;

[0126] The identity authentication module is used to perform user identity authentication;

[0127] The interactive interface module is used to provide an interactive interface between the user and the smart contract and data storage layer;

[0128] The data query module is used to query the data that the user has the authority to access;

[0129] The dynamic authorization and trust scoring module includes a behavior recording unit and a scoring calculation unit;

[0130] The behavior recording unit is used to record the user's interactive behavior and transaction history;

[0131] The score calculation unit is used to calculate the user's trust score, and the calculation formula is:

[0132] S trust =α×R interactions +β×T history +γ×F feedback

[0133] Among them, S trust represents the trust score, R interactions represents the user's interactive behavior (such as the number of interactions between the user and the system, the complexity of the operation), T history represents the user's transaction history (such as the number of transactions and success rate of the user in the past), F feedback represents community feedback (such as other users’ evaluation of the user and the trust report generated by the system), α, β and γ are the weight coefficients of each factor;

[0134] The revocable access rights module includes a behavior analysis unit and a dynamic adjustment unit;

[0135] The behavior analysis unit is used to collect and analyze the user's operation behavior, establish a behavior pattern model, and establish parameters including: user operation frequency F, operation complexity C, historical trust score S trust , the establishment steps are as follows:

[0136] S1. Data collection: Collect all user operations in the system, including but not limited to login, data access, permission application, operation frequency, collect contextual information related to user behavior, such as time, location, and device information, and finally obtain the user's operation history in the past period of time to establish a baseline behavior pattern;

[0137] S2. Data preprocessing: Remove invalid data, abnormal data, and noise data to ensure the accuracy of the analysis, and then convert different types of behavioral data into a unified standard form, such as standardizing time data into a unified time unit and standardizing operation frequency into the number of operations / unit time;

[0138] S3, Feature extraction: Extract key behavior features from the collected data, such as operation frequency F, operation complexity C, average response time R, amount of sensitive data accessed D, and extract contextual features related to user behavior, such as operation time period and device type used;

[0139] S4. Behavior pattern modeling: Based on the extracted behavior features, a clustering algorithm is used to cluster the user behavior data to identify different behavior pattern types, and then a random forest algorithm is selected for modeling. The selected model is trained using historical behavior data to obtain a preliminary behavior pattern model.

[0140] S6. Model verification and adjustment: Use the test data set to verify the accuracy and reliability of the model, calculate the model's accuracy, recall rate, and F1 value performance indicators, and optimize and adjust the model based on the verification results, which may include feature selection and parameter adjustment;

[0141] S7. Model deployment and real-time update: Deploy the verified behavior pattern model to the behavior analysis unit for real-time analysis of user operation behavior. Update the model regularly or in real time to adapt to changes in user behavior. Keep the model up-to-date and accurate through incremental learning or online learning mechanisms.

[0142] The dynamic adjustment unit dynamically adjusts the permission revocation conditions based on the following algorithm:

[0143] P revoke =f(S trust ,B)

[0144] Among them, P revoke The probability S of revoking permission trust is the current trust score, and B is the user's behavior pattern.

[0145] Specifically, the user completes identity authentication through the identity authentication module of the user layer. After the authentication is passed, the interactive interface module provides the user with an interactive interface between the smart contract layer and the data storage layer, allowing the user to submit requests or access data. The user's interactive behavior and transaction history in the system are recorded in real time by the behavior recording unit, and the user's trust score is calculated by the score calculation unit. The trust score is calculated by the formula, where S trust represents the user's trust score, R interactions represents the user's interactive behavior (such as the number of interactions, operation complexity), T historyIndicates the user's transaction history (such as transaction quantity, success rate), F feedback represents community feedback (such as user evaluation and system-generated reports), α, β, and γ are weight coefficients of each factor. After the score calculation is completed, the trust score will affect the dynamic adjustment unit in the revocable access permission module. This module collects and analyzes the user's operation behavior through the behavior analysis unit to establish a behavior pattern model. The behavior pattern modeling process includes data collection, preprocessing, feature extraction, behavior pattern modeling, model verification and adjustment, and model deployment and real-time update steps. First, the system collects relevant data from the user's operation behavior, and extracts key behavior features after preprocessing, such as operation frequency F, operation complexity C, and average response time R. Based on these features, clustering algorithm and random forest algorithm are used to model the behavior pattern. After verification and optimization, the model is deployed to the behavior analysis unit to monitor user behavior in real time. Finally, the dynamic adjustment unit is based on the current trust score S trust Dynamically adjust the permission revocation conditions with behavior pattern BB and calculate the probability P of permission revocation revoke And make corresponding behavioral analysis unit decisions. The unit modules work closely together through the data flow and instruction flow within the system to achieve real-time monitoring of user behavior and dynamic adjustment of authority management, ensuring the security and flexibility of the system during operation.

[0146] Please refer to the attached Fig. 9 -Attached Fig.11 , the data tampering detection and reporting module includes a data monitoring unit and a cross-validation unit;

[0147] The data monitoring unit is used to monitor the data status on and off the chain in real time;

[0148] The cross-validation unit, the cross-validation process comprises the following steps:

[0149] S1. Obtain data hash values ​​of different chains;

[0150] S2. Perform cross validation using the following formula:

[0151] V cross =H(D chain1 )⊕H(D chain2 )

[0152] Where V cross represents the cross validation result, H(D chain1 ) and H(D chain2 ) represent the data hash values ​​of chain 1 and chain 2 respectively;

[0153] If V cross =0, the data is consistent; otherwise, a data tampering report is generated;

[0154] The decentralized identity management module includes a basic verification unit, an advanced verification unit, and an inheritance and transfer unit;

[0155] The basic authentication unit is used to perform basic identity authentication, which includes but is not limited to password and biometrics;

[0156] The advanced authentication unit is used to perform advanced identity authentication, which includes but is not limited to multi-factor authentication;

[0157] The inheritance and transfer unit is used to support the secure transfer and inheritance of identity information;

[0158] The on-chain audit module includes a compliance check unit, an audit log unit, and a decision support unit;

[0159] The compliance checking unit is used to check the on-chain data according to predefined compliance rules;

[0160] The audit log unit is used to generate an audit log and store it on the blockchain for traceability;

[0161] The decision support unit generates management suggestions according to the following formula:

[0162]

[0163] Among them, D support To support decision making, i A is the weight of each audit result. i The corresponding audit results.

[0164] Specifically, the data tampering detection and reporting module first monitors the data status on and off the chain in real time through the data monitoring unit to ensure the integrity and consistency of the data in different storage and transmission stages. When the system detects that data consistency verification is required, the cross-verification unit is started, first obtaining the data hash values ​​of different blockchains (such as the hash value H(D of chain 1) chain1 ) and the hash value H(D chain2 )), and then by the formula Perform cross validation, if V cross=0 indicates that the data is consistent, otherwise a data tampering report is generated and an alarm is triggered. At the same time, the basic verification unit in the decentralized identity management module performs preliminary user identity authentication, such as password and biometrics. If the basic verification is passed, the advanced verification unit performs more stringent verification (such as multi-factor authentication) to ensure the authenticity and security of the user's identity. After the identity authentication is passed, the inheritance and transfer unit supports the secure transfer and inheritance of identity information to ensure the secure transmission of user identity information between different devices or users. When the data operation or identity authentication is passed, the on-chain audit module starts working. The compliance check unit performs a detailed check of the on-chain data according to the predefined compliance rules to ensure that all operations meet the established compliance standards. After the compliance check is completed, the audit log unit generates an audit log and stores it securely on the blockchain to ensure the transparency and traceability of the audit process. Finally, the decision support unit generates an audit log according to the formula Generate management recommendations, where D support represents the final generated management suggestion, w i is the weight of each audit result, A i For the corresponding audit results, through the collaborative operation of these modules, the system can effectively protect the privacy and security of e-government while ensuring data integrity, user identity security, compliance checks, and decision support.

[0165] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. The e-government privacy security protection system based on blockchain technology is characterized by: It includes blockchain layer, data storage layer, smart contract layer, user layer, privacy computing module, dynamic authorization and trust scoring module, revocable access rights module, data tampering detection and reporting module, decentralized identity management module and on-chain audit module; The blockchain layer is used to record and store data and transactions generated in the e-government process; The data storage layer is used to store encrypted sensitive data and store corresponding hash values ​​and indexes on the blockchain; The smart contract layer is used to define and execute rules and processes related to e-government; The user layer is used to interact with the system through decentralized applications; The privacy computing module is used to protect data privacy when multiple departments collaborate to process data, and the privacy computing module includes a computing capability evaluation unit and a computing strategy adjustment unit; The computing capacity evaluation unit is used to evaluate the computing resources and data characteristics of each participant; The computing strategy adjustment unit dynamically adjusts the computing task allocation based on the evaluation result, and the adjustment process includes the following steps: S1. Establish a computing capability model, the parameters of which include: computing resource quantity RR, task execution time TT, and data characteristic coefficient DD; S2. Calculation strategy optimization, calculate the optimal allocation plan through the following formula: Among them, C opt Represents the optimized computing power, R i represents the computing resources of the ith participant, W i Indicates the calculation weight, T i Indicates the task execution time; The dynamic authorization and trust scoring module is used to evaluate the user's credibility in real time based on the user's behavior and on-chain records, and dynamically adjust the user's permissions; The revocable access permission module is used to dynamically adjust the revocation conditions of permissions according to user behavior patterns; The data tampering detection and reporting module is used to monitor and report data tampering behavior; The decentralized identity management module is used to support multi-level verification and management of user identities; The on-chain audit module is used to perform complex compliance checks while protecting data privacy.

2. According to claim 1, the electronic government privacy and security protection system based on blockchain technology is characterized in that: The blockchain layer includes a transaction record module, a timestamp module, a data verification module and a consensus algorithm module; The transaction record module is used to record all transactions generated by user operations; The timestamp module is used to generate a timestamp for each transaction; The data verification module is used to verify data integrity through a hash algorithm; The consensus algorithm module is used to reach a consensus in the blockchain network through a consensus algorithm, and the consensus algorithm includes PoW, PoS and other consensus mechanisms applicable to the alliance chain.

3. According to claim 1, the electronic government privacy and security protection system based on blockchain technology is characterized in that: The data storage layer includes an encryption module, a decryption module and a hash generation module; The encryption module is used to encrypt sensitive data, and the encryption module adopts an asymmetric encryption algorithm; The decryption module is used to decrypt stored data for authorized users; The hash generation module is used to generate and store the hash value of the data.

4. The e-government privacy and security protection system based on blockchain technology according to claim 1 is characterized in that: The smart contract layer includes a contract execution module and a rule management module; The contract execution module is used to execute a predefined smart contract; The rule management module is used to manage and update the rules in the smart contract. The rules in the smart contract include but are not limited to defining new rules, updating existing rules, rule review and verification, and rule archiving and version control.

5. According to claim 1, the electronic government privacy and security protection system based on blockchain technology is characterized in that: The user layer includes an identity authentication module, an interactive interface mode and a data query module; The identity authentication module is used to perform user identity authentication; The interactive interface module is used to provide an interactive interface between the user and the smart contract and data storage layer; The data query module is used to query the data that the user has permission to access.

6. The e-government privacy and security protection system based on blockchain technology according to claim 1 is characterized in that: The dynamic authorization and trust scoring module includes a behavior recording unit and a scoring calculation unit; The behavior recording unit is used to record the user's interactive behavior and transaction history; The score calculation unit is used to calculate the user's trust score, and the calculation formula is: S trust =α×R interactions +β×T history +γ×F feedback Among them, S trust represents the trust score, R interactions represents the user's interactive behavior (such as the number of interactions between the user and the system, the complexity of the operation), T history represents the user's transaction history (such as the number of transactions and success rate of the user in the past), F feedback represents community feedback (such as other users' evaluation of the user and the trust report generated by the system), and α, β and γ are the weight coefficients of each factor.

7. The e-government privacy and security protection system based on blockchain technology according to claim 1 is characterized in that: The revocable access rights module includes a behavior analysis unit and a dynamic adjustment unit; The behavior analysis unit is used to collect and analyze the user's operation behavior, establish a behavior pattern model, and establish parameters including: user operation frequency F, operation complexity C, historical trust score S trust , the establishment steps are as follows: S1. Data collection: Collect all user operations in the system, including but not limited to login, data access, permission application, operation frequency, collect contextual information related to user behavior, such as time, location, and device information, and finally obtain the user's operation history in the past period of time to establish a baseline behavior pattern; S2. Data preprocessing: Remove invalid data, abnormal data, and noise data to ensure the accuracy of the analysis, and then convert different types of behavioral data into a unified standard form, such as standardizing time data into a unified time unit and standardizing operation frequency into the number of operations / unit time; S3, Feature extraction: Extract key behavior features from the collected data, such as operation frequency F, operation complexity C, average response time R, amount of sensitive data accessed D, and extract contextual features related to user behavior, such as operation time period and device type used; S4. Behavior pattern modeling: Based on the extracted behavior features, a clustering algorithm is used to cluster the user behavior data to identify different behavior pattern types, and then a random forest algorithm is selected for modeling. The selected model is trained using historical behavior data to obtain a preliminary behavior pattern model. S6. Model verification and adjustment: Use the test data set to verify the accuracy and reliability of the model, calculate the model's accuracy, recall rate, and F1 value performance indicators, and optimize and adjust the model based on the verification results, which may include feature selection and parameter adjustment; S7. Model deployment and real-time update: Deploy the verified behavior pattern model to the behavior analysis unit for real-time analysis of user operation behavior. Update the model regularly or in real time to adapt to changes in user behavior. Keep the model up-to-date and accurate through incremental learning or online learning mechanisms. The dynamic adjustment unit dynamically adjusts the permission revocation conditions based on the following algorithm: P revoke =f(S trust ,B) Among them, P revoke The probability S of revoking permission trust is the current trust score, and B is the user's behavior pattern.

8. The e-government privacy and security protection system based on blockchain technology according to claim 1 is characterized in that: The data tampering detection and reporting module includes a data monitoring unit and a cross-verification unit; The data monitoring unit is used to monitor the data status on and off the chain in real time; The cross-validation unit, the cross-validation process comprises the following steps: S1. Obtain data hash values ​​of different chains; S2. Perform cross validation using the following formula: V cross =H(D chain1 )⊕H(D chain2 ) Where V cross represents the cross-validation result, H(D chain1 ) and H(D chain2 ) represent the data hash values ​​of chain 1 and chain 2 respectively; If V cross =0, the data is consistent; otherwise, a data tampering report is generated.

9. The e-government privacy and security protection system based on blockchain technology according to claim 1 is characterized in that: The decentralized identity management module includes a basic verification unit, an advanced verification unit, and an inheritance and transfer unit; The basic authentication unit is used to perform basic identity authentication, which includes but is not limited to password and biometrics; The advanced authentication unit is used to perform advanced identity authentication, which includes but is not limited to multi-factor authentication; The inheritance and transfer unit is used to support the safe transfer and inheritance of identity information.

10. The e-government privacy and security protection system based on blockchain technology according to claim 1 is characterized in that: The on-chain audit module includes a compliance check unit, an audit log unit, and a decision support unit; The compliance checking unit is used to check the on-chain data according to predefined compliance rules; The audit log unit is used to generate an audit log and store it on the blockchain for traceability; The decision support unit generates management suggestions according to the following formula: Among them, D support To support decision making, i A is the weight of each audit result. i The corresponding audit results.

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