Blockchain-based enterprise information consulting service security verification platform

Abstract

The application discloses a blockchain-based enterprise information consulting service security verification platform and relates to the technical field of information security and privacy protection.The present application verifies data authenticity and performs desensitization processing by using zero-knowledge proof algorithm, solves the problems of data island and lack of trust mechanism in traditional platforms, protects enterprise privacy and breaks data barriers;combining homomorphic encryption and distributed storage technology, and the security multi-party computing algorithm of the collaborative analysis module, solves the problems of difficult data integration and low cooperation efficiency in traditional platforms, realizes data privacy protection and efficient cross-enterprise cooperation;through the smart contract mechanism and the tamper-proof storage of the blockchain, combined with the trust evaluation mechanism of the result fusion and evaluation module, the cost of trust establishment in the traditional verification method is solved, the trust cost is reduced, the benign circulation of the consulting service network is promoted, and the service efficiency and quality stability are improved.

Description

Technical Field

[0001] This invention relates to the field of information security and privacy protection technology, specifically a blockchain-based enterprise information consulting service security verification platform. Background Technology

[0002] Enterprise information consulting services, as a crucial support of the modern business ecosystem, play a key role in promoting optimized corporate decision-making, risk management, and market expansion. With the deepening of digital transformation, enterprises are increasingly demanding high-quality, reliable information consulting services, and technological innovation in this field has become a significant driver for improving business efficiency and competitiveness.

[0003] Currently, enterprise information consulting services mainly rely on traditional centralized platforms and manual verification mechanisms. These solutions have shown significant shortcomings when facing complex multi-party collaboration scenarios. Existing systems generally suffer from data silos, making it difficult to effectively integrate the information resources of various participants. This limits the depth and breadth of consulting services. At the same time, traditional verification methods lack a unified trust mechanism, and the cost of establishing trust between service providers and clients is high, affecting service efficiency and quality. Summary of the Invention

[0004] The purpose of this invention is to provide a blockchain-based secure verification platform for enterprise information consulting services, which realizes data privacy protection, efficient cross-enterprise collaboration and the establishment of a dynamic trust system, significantly improving service quality and efficiency.

[0005] The objective of this invention can be achieved through the following technical solutions:

[0006] This application provides a blockchain-based security verification platform for enterprise information consulting services, including:

[0007] The data verification and desensitization module obtains the original consultation data submitted by the enterprise, uses a zero-knowledge proof algorithm to verify the authenticity of the original consultation data, and identifies and separates sensitive fields in the original consultation data to obtain a desensitized dataset containing authenticity proof credentials.

[0008] The encryption and distributed storage module encrypts the de-identified dataset using a homomorphic encryption algorithm based on the authenticity proof credentials in the de-identified dataset, generating ciphertext verification data and obtaining encrypted verification data for distributed storage.

[0009] The collaborative analysis module uses a secure multi-party computation algorithm to collaboratively analyze and process encrypted verification data. Each participant performs joint computation operations without decrypting the original data, and records the computational contribution information of each party.

[0010] The results fusion and evaluation module assesses the credibility of preliminary results data through a pre-established trust evaluation mechanism, and then further fuses the preliminary results data, using a weighted fusion algorithm to combine the contribution information of each party to obtain comprehensive analysis results.

[0011] The privacy protection and report generation module, based on the comprehensive analysis results, uses differential privacy technology to add noise to the parts of the results that reveal sensitive information of the participants. If the results after adding noise still maintain the preset analysis accuracy requirements, the final consulting service report is generated.

[0012] The incentive and trust rating module, after obtaining the consulting service report, automatically evaluates the data contribution and service quality of each participant through a smart contract mechanism. If the evaluation results meet the preset standards, the corresponding incentive distribution mechanism is triggered, and the trust rating and collaboration history of each party are updated.

[0013] Furthermore, the data verification and desensitization module includes:

[0014] The system obtains the original consultation data submitted by enterprises, extracts the data structure and content through the data parsing module to obtain structured data records, and uses a zero-knowledge proof algorithm to verify the authenticity of the structured data records and generate verification results.

[0015] If the verification result passes the integrity check of the preset verification rules, an authenticity certificate is generated, and a dataset containing the certificate is obtained. The dataset containing the certificate is then scanned by a sensitive field identification model to identify sensitive fields, resulting in a labeled dataset.

[0016] Based on the sensitive field identifiers in the labeled dataset, a data separation algorithm is used to extract the sensitive fields, resulting in a separated dataset and a sensitive field set. The separated dataset is then de-identified to generate a de-identified dataset containing proof of authenticity.

[0017] Furthermore, the encryption and distributed storage module includes:

[0018] Obtain the authenticity verification credentials from the de-identified dataset, extract key identifier fields by parsing the credential structure to obtain standardized credential data, and encrypt the standardized credential data using a homomorphic encryption algorithm to generate ciphertext verification data;

[0019] Determine whether the encrypted verification data meets the preset encryption strength threshold. If it does, determine that the verification data meets the distribution requirements. Then, transmit the verification data that meets the distribution requirements to each enterprise node through the distributed protocol to obtain the encrypted verification data received by the node.

[0020] The encrypted verification data is distributed and stored on each enterprise node. A hash index is used to generate a storage address mapping to obtain a distributed storage index. By performing consistency verification on the distributed storage index, it is determined whether the index integrity meets the preset consistency threshold, and the encrypted verification data in the distributed storage is determined.

[0021] Furthermore, the collaborative analysis module includes:

[0022] The encrypted verification data is preprocessed using a secure multi-party computation algorithm to obtain encrypted data fragments uploaded by each participant, confirm that the data fragments conform to the preset format standard, and perform joint computation operations on the encrypted data fragments using a homomorphic encryption algorithm to obtain intermediate computation results.

[0023] If the intermediate calculation results meet the preset threshold of the consistency verification mechanism, the secret sharing algorithm is used to distribute the intermediate calculation results, generate preliminary collaborative analysis results, and then calculate the calculation contribution of each participant to obtain contribution weight information.

[0024] The contribution weight information is verified by a verifiable computation protocol to determine the verified contribution data. A zero-knowledge proof protocol is then used to protect the privacy of the verified contribution data, resulting in the final collaborative analysis output. Encrypted log records are generated to store the contribution information and analysis result metadata of each participant.

[0025] Furthermore, the computational contribution of each participant is calculated based on a weighted summation model using multi-dimensional indicators.

[0026] Represented as: ,in It is the first The contribution weight of each participant, It is the first The weight of each indicator, It is the first The participating party in the first Normalized values ​​for each indicator It is the first The participating party in the first The original values ​​of each indicator and They are the first The minimum and maximum values ​​of each indicator. Metrics for measuring dimensions.

[0027] Furthermore, the result fusion and evaluation module includes:

[0028] Preliminary result data is obtained from the data input source, a trust assessment model is applied to calculate the credibility score to obtain the preliminary credibility score, and then a weighted fusion algorithm is used to process the preliminary result data and combine it with the contribution weight vector to obtain the weighted fusion data.

[0029] By performing consistency verification on the weighted fused data to determine data integrity, the verified fused data is obtained. The output format of the application results is standardized to generate comprehensive analysis results. Key indicators are extracted from the comprehensive analysis results and stored in the database to obtain persistent analysis results.

[0030] Furthermore, the privacy protection and report generation module includes:

[0031] To obtain comprehensive analysis results, fields containing sensitive information are extracted from the dataset. Differential privacy technology is used to calculate noise parameters, determine the sensitive information part to be processed, add Gaussian noise to the sensitive information part using differential privacy technology, and generate the processed analysis results.

[0032] If the deviation between the processed analysis result and the original result is less than the preset analysis accuracy requirement, the result is determined to meet the accuracy standard, and a compliant processing result is obtained. A structured consulting service report is generated, which includes the analysis conclusion and the processed data fields.

[0033] Metadata from each processing step in the consulting service report generation process is extracted to construct a traceability chain record, including data source, noise addition parameters, and accuracy verification information. Through the traceability chain record, the integrity of the processing process is verified, and an encrypted consulting service report is generated.

[0034] Furthermore, the incentive and trust rating module includes:

[0035] The data contribution and service quality information of each participant are obtained from the consulting service report through smart contracts. The data contribution is quantified and classified using the support vector machine algorithm. The service quality information is analyzed through preset service quality evaluation rules to obtain a service quality score.

[0036] If both the data contribution score and the service quality score meet the preset standards, the incentive allocation mechanism will be triggered through the smart contract to generate incentive allocation records. The trust rating of each participant will be updated using a weighted average algorithm to generate updated trust rating data.

[0037] By using blockchain technology, the updated trust rating data and incentive allocation records of all parties are written into the collaboration history record, generating new collaboration history data. Logistic regression algorithm is used to analyze the collaboration patterns of the participants and obtain collaboration trend analysis results.

[0038] Furthermore, it also includes a record storage and optimization module, which uses blockchain technology to store the collaborative history in an immutable manner. If the storage operation is successfully completed, the updated trust rating information will be synchronized to the entire consulting service network to form a continuously optimized multi-party collaborative trust system, supporting the improvement of the quality and efficiency of subsequent consulting services.

[0039] Furthermore, the recording storage and optimization module includes:

[0040] Blockchain technology is used to store the collaboration history in an immutable manner. Trust records are then obtained from the distributed ledger to verify their unique identifier and integrity. If the verification is successful, the interaction data in the collaboration history is extracted to determine the basic information for trust rating.

[0041] Based on the collaborative behavior of the participants in the interaction data analysis, the trust rating is calculated using the support vector machine algorithm, and the updated trust rating information is generated. The updated trust rating information is then synchronized to each node of the consulting service network through a smart contract to obtain consistent trust data across the entire network.

[0042] The system obtains synchronized trust data from the consulting service network, analyzes the dynamic optimization trend of multi-party collaboration, uses the Naive Bayes algorithm to predict collaboration efficiency, determines the optimization direction, and adjusts the storage strategy of collaboration history based on the optimization direction.

[0043] If the trust rating is higher than the preset threshold, high-trust interaction data is stored first, a simplified collaboration record is generated, and the simplified collaboration record is verified through a distributed ledger. If the verification is successful, the collaboration rules of the consulting service network are updated to obtain an optimized multi-party collaboration trust system.

[0044] The beneficial effects of this invention are as follows:

[0045] The data verification and de-identification module employs a zero-knowledge proof algorithm to verify data authenticity, identify and separate sensitive fields, and generate a de-identified dataset. This solves the problems of data silos and lack of trust mechanisms in traditional platforms. The zero-knowledge proof algorithm completes authenticity verification without disclosing the original data, and the generated proof credentials provide a unified basis for data credibility, reducing the cost of establishing trust between the service parties. At the same time, the separation and de-identification of sensitive fields not only protects corporate privacy but also breaks down data barriers, enabling the integration of information resources from all parties under secure conditions. This expands the depth and breadth of consulting services and enhances the basic guarantee capabilities for service quality.

[0046] By employing homomorphic encryption algorithms and distributed storage technology in the encryption and distributed storage modules, along with secure multi-party computation algorithms in the collaborative analysis module, the problems of difficult data integration and low collaborative analysis efficiency in traditional centralized platforms are solved. Homomorphic encryption ensures that data can still be processed after being encrypted and de-identified, while distributed storage disperses data across nodes, avoiding single points of failure and data monopoly. Secure multi-party computation allows participants to complete joint analysis without decrypting the data, protecting data privacy while enabling efficient cross-enterprise collaboration. This makes in-depth analysis, which was previously impossible due to data isolation, possible, significantly improving the efficiency and security of multi-party collaboration in consulting services.

[0047] By leveraging the smart contract mechanism of the incentive and trust rating module, the immutable blockchain storage of the record storage and optimization module, and the trust assessment mechanism of the result fusion and evaluation module, this approach solves the problems of high trust establishment costs and unreliable traceability of collaboration history in traditional verification methods. Smart contracts automatically evaluate the contributions of participants and trigger incentives, while blockchain storage ensures the immutability of collaboration history and trust ratings, forming a dynamically updated trust system. The trust assessment mechanism provides credibility endorsement for the analysis results, enabling service providers and consumers to cooperate based on reliable trust records, reducing trust costs. At the same time, the continuously optimized trust system promotes a virtuous cycle in the consulting service network, further improving service efficiency and quality stability. Attached Figure Description

[0048] To better understand and implement this application, the technical solution is described in detail below with reference to the accompanying drawings.

[0049] Figure 1 A flowchart illustrating the security verification platform for blockchain-based enterprise information consulting services provided in this application;

[0050] Figure 2 A flowchart illustrating the collaborative analysis module of the blockchain-based enterprise information consulting service security verification platform provided in this application;

[0051] Figure 3 A flowchart illustrating the incentive and trust rating module of the blockchain-based enterprise information consulting service security verification platform provided in this application. Detailed Implementation

[0052] To further illustrate the technical means and effects adopted by the present invention to achieve its intended purpose, exemplary embodiments will be described in detail below, examples of which are illustrated in the accompanying drawings. In the following description relating to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of methods and systems consistent with some aspects of this application as detailed in the appended claims.

[0053] The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The singular forms “a,” “the,” and “the” used in this application and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise. It should also be understood that the term “and / or” as used herein refers to and includes any or all possible combinations of one or more of the associated listed items.

[0054] The following detailed description of the specific implementation methods, features, and effects of the present invention, in conjunction with the accompanying drawings and preferred embodiments, is provided in detail.

[0055] Please see Figures 1-3 This embodiment provides a blockchain-based enterprise information consulting service security verification platform, including:

[0056] The data verification and desensitization module obtains the original consultation data submitted by the enterprise and uses a zero-knowledge proof algorithm to verify the authenticity of the original consultation data. If the original consultation data passes the integrity check of the preset verification rules, a corresponding authenticity certificate is generated. At the same time, sensitive fields in the original consultation data are identified and separated to obtain a desensitized dataset containing the authenticity certificate.

[0057] Furthermore, the data verification and desensitization module includes:

[0058] The system obtains the original consultation data submitted by enterprises, extracts the data structure and content through the data parsing module to obtain structured data records, and uses a zero-knowledge proof algorithm to verify the authenticity of the structured data records and generate verification results.

[0059] The data parsing module is the core component of the data verification and desensitization module. It uses natural language processing (NLP) and data mining techniques to perform structured processing on the submitted original consultation data. Specifically, the data parsing module can identify key information, fields and relationships in the data and transform them into standardized structured data records, providing a clear and accurate data foundation for subsequent authenticity verification and privacy protection operations.

[0060] When using zero-knowledge proof algorithms to verify the authenticity of structured data records, the structured data is first converted into a mathematical form suitable for zero-knowledge proofs. Then, using a zero-knowledge proof protocol, the proving party (data provider) generates a proof through a series of cryptographic calculations. The verifying party (platform) confirms the authenticity of the data only by verifying the validity of the proof, without obtaining the specific content of the data. This process ensures the verification of data authenticity while protecting data privacy.

[0061] If the verification result passes the integrity check of the preset verification rules, an authenticity certificate is generated, and a dataset containing the certificate is obtained. The dataset containing the certificate is then scanned by a sensitive field identification model to identify sensitive fields, resulting in a labeled dataset.

[0062] Based on the sensitive field identifiers in the labeled dataset, a data separation algorithm is used to extract the sensitive fields, resulting in a separated dataset and a sensitive field set. The separated dataset is then de-identified to generate a de-identified dataset containing proof of authenticity.

[0063] Specifically, zero-knowledge proof algorithms are used to verify the authenticity of data. Combined with data parsing and sensitive field processing techniques, de-identified datasets are generated, effectively solving the problems of data authenticity and privacy protection. This ensures that the data is trustworthy and secure, providing a reliable foundation for subsequent analysis and improving the security and credibility of consulting services.

[0064] The encryption and distributed storage module encrypts the de-identified dataset using a homomorphic encryption algorithm based on the authenticity proof credentials in the de-identified dataset, generating ciphertext verification data. If the verification data meets the preset encryption strength requirements, it distributes the verification data to each participating enterprise node to obtain encrypted verification data in distributed storage.

[0065] Furthermore, the encryption and distributed storage module includes:

[0066] Obtain the authenticity verification credentials from the de-identified dataset, extract key identifier fields by parsing the credential structure to obtain standardized credential data, and encrypt the standardized credential data using a homomorphic encryption algorithm to generate ciphertext verification data;

[0067] Determine whether the encrypted verification data meets the preset encryption strength threshold. If it does, determine that the verification data meets the distribution requirements. Then, transmit the verification data that meets the distribution requirements to each enterprise node through the distributed protocol to obtain the encrypted verification data received by the node.

[0068] The encrypted verification data is distributed and stored on each enterprise node. A hash index is used to generate a storage address mapping to obtain a distributed storage index. By performing consistency verification on the distributed storage index, it is determined whether the index integrity meets the preset consistency threshold, and the encrypted verification data in the distributed storage is determined.

[0069] In this distributed storage mechanism, when enterprise nodes store encrypted verification data, the data is sharded according to preset rules, so that each node stores only a portion of the data shards to achieve distributed storage. For each data shard stored by each node, a hash algorithm (such as SHA-256) is used to calculate its unique hash value, and the specific physical storage address of the shard within the node (such as disk path, memory location, etc.) is recorded. The correspondence between "hash value and storage address" is then integrated to form a distributed storage index containing the location mapping of all data shards across the entire network. Subsequently, through periodic synchronization between nodes, the local storage index of each node is shared with other nodes, and a consensus algorithm (such as PBFT, Raft, etc.) is used to compare and verify the entire network index. If the index of a certain node differs from that of most nodes (such as hash value mismatch, address missing, etc.), the index of that node is determined to be abnormal, and a data repair mechanism is triggered to synchronize the correct data shards and corresponding index information from other nodes until the distributed storage indexes of all nodes reach consistency and meet the preset consistency threshold, thereby ensuring the integrity and accuracy of the index and providing reliable support for the rapid location and access of subsequent data.

[0070] Specifically, by employing homomorphic encryption, distributed protocols, hash indexes, and consistency checks, de-identified data containing authenticity certificates is encrypted and then distributed to various enterprise nodes for storage. This solves the problems of tampering risk, single point of failure, and difficulty in collaboration after encryption in traditional centralized storage, ensuring data security, integrity, and computability, and providing efficient support for cross-enterprise collaboration.

[0071] The collaborative analysis module uses a secure multi-party computation algorithm to perform collaborative analysis on encrypted verification data. Each participant performs joint computation operations without decrypting the original data. If the intermediate results generated by the joint computation operation pass the consistency verification, preliminary results data of the collaborative analysis are generated, and the computational contribution information of each party is recorded.

[0072] Furthermore, the collaborative analysis module includes:

[0073] S11. The encrypted verification data is preprocessed using a secure multi-party computation algorithm to obtain the encrypted data fragments uploaded by each participant, and the data fragments are determined to conform to the preset format standard. The encrypted data fragments are then subjected to joint computation operations using a homomorphic encryption algorithm to obtain intermediate computation results.

[0074] S12. If the intermediate calculation results meet the preset threshold of the consistency verification mechanism, the secret sharing algorithm is used to distribute the intermediate calculation results, generate preliminary collaborative analysis results, and then calculate the calculation contribution of each participant to obtain contribution weight information.

[0075] When using the secret sharing algorithm, the intermediate calculation results are first decomposed into multiple interrelated sub-shares according to preset splitting rules. This ensures that a single sub-share cannot restore the complete result, and that a preset number of sub-shares must be aggregated to recover the original data. The sub-shares are then distributed to different enterprise nodes participating in the collaboration via encrypted transmission for distributed storage. Each node only stores the sub-shares it receives, avoiding single-point storage risks. Next, a sufficient number of sub-shares are aggregated through a secure communication protocol between nodes to restore the complete intermediate calculation results. These results are then integrated with the target dimensions of collaborative analysis (such as data association rules and trend characteristics) to generate preliminary collaborative analysis results. Afterward, based on quantitative indicators such as the data sharding scale provided by each participant, the computing resource investment of computing nodes, the duration of participation in computation, and the contribution of intermediate results, a preset weighted calculation model (such as combining data value coefficients and computational efficiency weights) is used to calculate the computational contribution value of each participant. Finally, based on the contribution value ratio of all participants, corresponding contribution weight information is obtained, providing a basis for subsequent result fusion and incentive allocation.

[0076] Furthermore, the computational contribution of each participant is calculated based on a weighted summation model using multi-dimensional indicators.

[0077] Represented as: ,in It is the first The contribution weight of each participant, It is the first The weight of each indicator, It is the first The participating party in the first Normalized values ​​for each indicator It is the first The participating party in the first The original values ​​of each indicator and They are the first The minimum and maximum values ​​of each indicator. Metrics for measuring dimensions.

[0078] S13. Verify the contribution weight information through a verifiable computation protocol, determine the verified contribution data, use a zero-knowledge proof protocol to perform privacy protection processing on the verified contribution data, obtain the final collaborative analysis output, generate encrypted log records, and store the contribution information and analysis result metadata of each participant.

[0079] Specifically, the collaborative analysis module enables all participants to complete joint calculations without decrypting the data. It ensures the reliability of the results through intermediate result verification and secret shared storage, while accurately measuring and protecting the contributions of each party. This solves the problems of balancing privacy protection and collaborative analysis, as well as the lack of transparency in contribution measurement. It achieves secure and efficient cross-enterprise collaborative analysis, and enhances the depth and credibility of consulting services.

[0080] The results fusion and evaluation module assesses the credibility of preliminary results data through a pre-established trust evaluation mechanism, and then further fuses the preliminary results data, using a weighted fusion algorithm to combine the contribution information of each party to obtain comprehensive analysis results.

[0081] Furthermore, the result fusion and evaluation module includes:

[0082] Preliminary result data is obtained from the data input source, a trust assessment model is applied to calculate the credibility score to obtain the preliminary credibility score, and then a weighted fusion algorithm is used to process the preliminary result data and combine it with the contribution weight vector to obtain the weighted fusion data.

[0083] The process involves obtaining preliminary results data from input sources, which may come from multiple participants or different analysis modules. A pre-established trust assessment model is then applied to score the credibility of each preliminary result data point. This model typically calculates the credibility score for each data point based on multiple dimensions, including the data source's reputation, historical performance, and data quality. Based on the contribution weight vectors of each participant, a weighted fusion algorithm is used to process the preliminary results data. Specifically, the credibility score of each data point is multiplied by its corresponding contribution weight, and the weighted results of all data are summarized to obtain the final weighted fused data. This process considers not only the credibility of the data but also the contributions of each participant, ensuring the scientific rigor and fairness of the fusion result.

[0084] By performing consistency verification on the weighted fused data to determine data integrity, the verified fused data is obtained. The output format of the application results is standardized to generate comprehensive analysis results. Key indicators are extracted from the comprehensive analysis results and stored in the database to obtain persistent analysis results.

[0085] Specifically, the credibility of preliminary results data is scored using a trust assessment model, and a weighted fusion algorithm is used to generate comprehensive analysis results by combining contribution weight vectors. The results are then persisted through consistency verification and standardization. This solves the problems of low credibility, inaccurate fusion, and insufficient standardization of results in traditional consulting services, thereby improving the reliability and efficiency of decision support.

[0086] The privacy protection and report generation module, based on the comprehensive analysis results, uses differential privacy technology to add noise to the parts of the results that reveal sensitive information of the participants. If the results after adding noise still maintain the preset analysis accuracy requirements, the final consulting service report is generated, and a result traceability chain is constructed to record the entire processing process.

[0087] Furthermore, the privacy protection and report generation module includes:

[0088] To obtain comprehensive analysis results, fields containing sensitive information are extracted from the dataset. Differential privacy technology is used to calculate noise parameters, determine the sensitive information part to be processed, add Gaussian noise to the sensitive information part using differential privacy technology, and generate the processed analysis results.

[0089] If the deviation between the processed analysis result and the original result is less than the preset analysis accuracy requirement, the result is determined to meet the accuracy standard, and a compliant processing result is obtained. A structured consulting service report is generated, which includes the analysis conclusion and the processed data fields.

[0090] Metadata from each processing step in the consulting service report generation process is extracted to construct a traceability chain record, including data source, noise addition parameters, and accuracy verification information. Through the traceability chain record, the integrity of the processing process is verified, and an encrypted consulting service report is generated.

[0091] Specifically, differential privacy technology is used to add noise to sensitive information portions of the comprehensive analysis results. This ensures that while protecting the privacy of participants, the results still meet the preset analytical accuracy requirements. This solves the problems of high privacy leakage risk and insufficient traceability in traditional consulting services. By accurately calculating noise parameters and adding Gaussian noise, sensitive information is protected while ensuring the usability of the analysis results. Simultaneously, a traceability chain is constructed to record the entire processing, ensuring the integrity and verifiability of the results. Finally, an encrypted consulting service report is generated, enhancing the report's security and credibility, and providing users with high-quality and privacy-secure consulting services.

[0092] The incentive and trust rating module, after obtaining the consulting service report, automatically evaluates the data contribution and service quality of each participant through a smart contract mechanism. If the evaluation results meet the preset standards, the corresponding incentive distribution mechanism is triggered, and the trust rating and collaboration history of each party are updated.

[0093] Furthermore, the incentive and trust rating module includes:

[0094] S21. Obtain data contribution and service quality information of each participant from the consulting service report through smart contracts, quantify and classify the data contribution using the support vector machine algorithm, analyze the service quality information through preset service quality evaluation rules, and obtain a service quality score.

[0095] The preset service quality assessment rules are quantitative standards set based on key indicators such as response time, task completion rate, and computing resource utilization. For example, the response time is less than 1 second, the task completion rate is more than 90%, and the computing resource utilization rate is higher than 80%, which are used to assess the service quality of the participants.

[0096] S22. If both the data contribution score and the service quality score meet the preset standards, the incentive allocation mechanism will be triggered through the smart contract to generate incentive allocation records. The trust rating of each participant will be updated using a weighted average algorithm to generate updated trust rating data.

[0097] If both the data contribution score and the service quality score meet the preset standards, the preset incentive allocation logic in the smart contract is triggered: The smart contract first reads the preset total amount of the incentive pool and the allocation rules (such as allocation according to the contribution weight ratio), calculates the incentive share that each participant should receive, and simultaneously generates an incentive allocation record containing the allocation object, amount, and timestamp and writes it to the blockchain; then, the trust rating is updated using a weighted average algorithm. Based on the participant's current trust rating, combined with the current data contribution score and service quality score (each assigned a preset weight, such as a contribution score weight of 0.6 and a service quality score weight of 0.4), the new rating is calculated using the formula "Updated Trust Rating = Original Trust Rating × Historical Weight + (Data Contribution Score × 0.6 + Service Quality Score × 0.4) × Current Weight", generating updated trust rating data to ensure that the rating can dynamically reflect the collaborative performance of the participants.

[0098] S23. Using blockchain technology, the updated trust rating data and incentive allocation records of all parties are written into the collaboration history record to generate new collaboration history data. Logistic regression algorithm is used to analyze the collaboration patterns of the participants and obtain collaboration trend analysis results.

[0099] Specifically, the system uses smart contracts to automatically evaluate the data contributions and service quality of each participant, and triggers an incentive distribution mechanism based on the evaluation results, updating trust ratings and collaboration history. This solves the problems of opaque incentive distribution, high trust establishment costs, and difficulty in tracing collaboration history in traditional enterprise information consulting services. Through smart contracts and blockchain technology, the system achieves quantitative evaluation of data contributions and service quality, ensuring the fairness and transparency of incentive distribution. At the same time, the dynamic updating of trust ratings and the immutable storage of collaboration history enhance the trust foundation of multi-party collaboration and improve the collaboration efficiency and credibility of the entire consulting service network.

[0100] The record storage and optimization module uses blockchain technology to store the collaborative history in an immutable manner. If the storage operation is successfully completed, the updated trust rating information will be synchronized to the entire consulting service network, forming a continuously optimized multi-party collaborative trust system to support the improvement of the quality and efficiency of subsequent consulting services.

[0101] Furthermore, the recording storage and optimization module includes:

[0102] Blockchain technology is used to store the collaboration history in an immutable manner. Trust records are then obtained from the distributed ledger to verify their unique identifier and integrity. If the verification is successful, the interaction data in the collaboration history is extracted to determine the basic information for trust rating.

[0103] Based on the collaborative behavior of the participants in the interaction data analysis, the trust rating is calculated using the support vector machine algorithm, and the updated trust rating information is generated. The updated trust rating information is then synchronized to each node of the consulting service network through a smart contract to obtain consistent trust data across the entire network.

[0104] The process involves extracting interaction data from the collaboration history, including features such as data contribution, response timeliness, and result accuracy. These features are then input into a trained support vector machine model, which calculates the trust rating score for each participant, generating updated trust rating information. Subsequently, the system calls a smart contract to write the new trust rating information into the blockchain. The smart contract automatically executes an information synchronization mechanism, pushing the data to all nodes in the consulting service network. After verification and confirmation by each node, consistent trust data is formed across the entire network, ensuring that the trust rating information obtained by all parties is real-time and unified.

[0105] The system obtains synchronized trust data from the consulting service network, analyzes the dynamic optimization trend of multi-party collaboration, uses the Naive Bayes algorithm to predict collaboration efficiency, determines the optimization direction, and adjusts the storage strategy of collaboration history based on the optimization direction.

[0106] The process involves acquiring synchronized trust data (including historical trust ratings, collaboration success rates, and response speeds of each participant) from the consulting service network. This data is then cleaned and feature-engineered to extract key features affecting collaboration efficiency (such as trust rating fluctuations, frequency of participation by high-trust nodes, and cross-node data interaction latency). These features are input into a trained Naive Bayes algorithm model. Using historical collaboration efficiency data (such as task completion time and result accuracy) as labels, the model calculates posterior probabilities to predict future efficiency performance under different collaboration modes. This identifies highly efficient collaboration combinations (such as prioritizing collaboration with high-trust nodes) and efficiency bottlenecks (such as latency issues when low-trust nodes participate), determining optimization directions (such as prioritizing the retention of high-trust node interaction records and streamlining low-value collaboration data). Finally, storage strategies are adjusted based on these optimization directions. For example, more storage resources are allocated to the collaboration history of high-trust nodes, and a fast-access index is established. Non-critical records from low-trust nodes are compressed or periodically archived. Simultaneously, dynamic threshold settings (such as retaining records with trust ratings below a certain value for only 3 months) balance storage efficiency and data availability.

[0107] If the trust rating is higher than the preset threshold, high-trust interaction data is stored first, a simplified collaboration record is generated, and the simplified collaboration record is verified through a distributed ledger. If the verification is successful, the collaboration rules of the consulting service network are updated to obtain an optimized multi-party collaboration trust system.

[0108] Specifically, by using blockchain technology to store collaboration history in an immutable manner, the problems of easily tampered collaboration history, untimely updates to trust ratings, and difficulty in optimizing collaboration efficiency in traditional enterprise information consulting services are solved. This forms a continuously optimized trust system, providing reliable support for improving the quality and efficiency of consulting services.

[0109] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or alterations to the above-disclosed technical content to create equivalent embodiments without departing from the scope of the present invention. Any simple modifications, equivalent changes and alterations made to the above embodiments based on the technical essence of the present invention without departing from the scope of the present invention shall still fall within the scope of the present invention.

Claims

1. A blockchain-based enterprise information consulting service security verification platform, characterized in that: include: The data verification and desensitization module obtains the original consultation data submitted by the enterprise, uses a zero-knowledge proof algorithm to verify the authenticity of the original consultation data, and identifies and separates sensitive fields in the original consultation data to obtain a desensitized dataset containing authenticity proof credentials. The data verification and de-identification module includes: The system obtains the original consultation data submitted by enterprises, extracts the data structure and content through the data parsing module to obtain structured data records, and uses a zero-knowledge proof algorithm to verify the authenticity of the structured data records and generate verification results. If the verification result passes the integrity check of the preset verification rules, an authenticity certificate is generated, and a dataset containing the certificate is obtained. The dataset containing the certificate is then scanned by a sensitive field identification model to identify sensitive fields, resulting in a labeled dataset. Based on the sensitive field identifiers in the labeled dataset, a data separation algorithm is used to extract the sensitive fields, resulting in a separated dataset and a sensitive field set. The separated dataset is then desensitized to generate a desensitized dataset containing proof of authenticity. The encryption and distributed storage module encrypts the de-identified dataset using a homomorphic encryption algorithm based on the authenticity proof credentials in the de-identified dataset, generating ciphertext verification data and obtaining encrypted verification data for distributed storage. The collaborative analysis module uses a secure multi-party computation algorithm to collaboratively analyze and process encrypted verification data. Each participant performs joint computation operations without decrypting the original data, and records the computational contribution information of each party. The results fusion and evaluation module assesses the credibility of preliminary results data through a pre-established trust evaluation mechanism, and then further fuses the preliminary results data, using a weighted fusion algorithm to combine the contribution information of each party to obtain comprehensive analysis results. The privacy protection and report generation module, based on the comprehensive analysis results, uses differential privacy technology to add noise to the parts of the results that reveal sensitive information of the participants. If the results after adding noise still maintain the preset analysis accuracy requirements, the final consulting service report is generated. The incentive and trust rating module, after obtaining the consulting service report, automatically evaluates the data contribution and service quality of each participant through a smart contract mechanism. If the evaluation results meet the preset standards, the corresponding incentive distribution mechanism is triggered, and the trust rating and collaboration history of each party are updated.

2. The blockchain-based enterprise information consulting service security verification platform according to claim 1, characterized in that: The encryption and distributed storage module includes: Obtain the authenticity verification credentials from the de-identified dataset, extract key identifier fields by parsing the credential structure to obtain standardized credential data, and encrypt the standardized credential data using a homomorphic encryption algorithm to generate ciphertext verification data; Determine whether the encrypted verification data meets the preset encryption strength threshold. If it does, determine that the verification data meets the distribution requirements. Then, transmit the verification data that meets the distribution requirements to each enterprise node through the distributed protocol to obtain the encrypted verification data received by the node. The encrypted verification data is distributed and stored on each enterprise node. A hash index is used to generate a storage address mapping to obtain a distributed storage index. By performing consistency verification on the distributed storage index, it is determined whether the index integrity meets the preset consistency threshold, and the encrypted verification data in the distributed storage is determined.

3. The blockchain-based enterprise information consulting service security verification platform according to claim 1, characterized in that: The collaborative analysis module includes: The encrypted verification data is preprocessed using a secure multi-party computation algorithm to obtain encrypted data fragments uploaded by each participant, confirm that the data fragments conform to the preset format standard, and perform joint computation operations on the encrypted data fragments using a homomorphic encryption algorithm to obtain intermediate computation results. If the intermediate calculation results meet the preset threshold of the consistency verification mechanism, the secret sharing algorithm is used to distribute the intermediate calculation results, generate preliminary collaborative analysis results, and then calculate the calculation contribution of each participant to obtain contribution weight information. The contribution weight information is verified by a verifiable computation protocol to determine the verified contribution data. A zero-knowledge proof protocol is then used to protect the privacy of the verified contribution data, resulting in the final collaborative analysis output. Encrypted log records are generated to store the contribution information and analysis result metadata of each participant.

4. The blockchain-based enterprise information consulting service security verification platform according to claim 3, characterized in that: The computational contribution of each participant is calculated using a weighted summation model based on multi-dimensional indicators. Represented as: ,in It is the first The contribution weight of each participant, It is the first The weight of each indicator, It is the first The participating party in the first Normalized values ​​for each indicator It is the first The participating party in the first The original values ​​of each indicator and They are the first The minimum and maximum values ​​of each indicator. Metrics for measuring dimensions.

5. The blockchain-based enterprise information consulting service security verification platform according to claim 1, characterized in that: The result fusion and evaluation module includes: Preliminary result data is obtained from the data input source, a trust assessment model is applied to calculate the credibility score to obtain the preliminary credibility score, and then a weighted fusion algorithm is used to process the preliminary result data and combine it with the contribution weight vector to obtain the weighted fusion data. By performing consistency verification on the weighted fused data to determine data integrity, the verified fused data is obtained. The output format of the application results is standardized to generate comprehensive analysis results. Key indicators are extracted from the comprehensive analysis results and stored in the database to obtain persistent analysis results.

6. The blockchain-based enterprise information consulting service security verification platform according to claim 1, characterized in that: The privacy protection and report generation module includes: To obtain comprehensive analysis results, fields containing sensitive information are extracted from the dataset. Differential privacy technology is used to calculate noise parameters, determine the sensitive information part to be processed, add Gaussian noise to the sensitive information part using differential privacy technology, and generate the processed analysis results. If the deviation between the processed analysis result and the original result is less than the preset analysis accuracy requirement, the result is determined to meet the accuracy standard, and a compliant processing result is obtained. A structured consulting service report is generated, which includes the analysis conclusion and the processed data fields. Metadata from each processing step in the consulting service report generation process is extracted to construct a traceability chain record, including data source, noise addition parameters, and accuracy verification information. Through the traceability chain record, the integrity of the processing process is verified, and an encrypted consulting service report is generated.

7. The blockchain-based enterprise information consulting service security verification platform according to claim 1, characterized in that: The incentive and trust rating module includes: The data contribution and service quality information of each participant are obtained from the consulting service report through smart contracts. The data contribution is quantified and classified using the support vector machine algorithm. The service quality information is analyzed through preset service quality evaluation rules to obtain a service quality score. If both the data contribution score and the service quality score meet the preset standards, the incentive allocation mechanism will be triggered through the smart contract to generate incentive allocation records. The trust rating of each participant will be updated using a weighted average algorithm to generate updated trust rating data. By using blockchain technology, the updated trust rating data and incentive allocation records of all parties are written into the collaboration history record, generating new collaboration history data. Logistic regression algorithm is used to analyze the collaboration patterns of the participants and obtain collaboration trend analysis results.

8. The blockchain-based enterprise information consulting service security verification platform according to claim 1, characterized in that: Also includes: The record storage and optimization module uses blockchain technology to store the collaborative history in an immutable manner. If the storage operation is successfully completed, the updated trust rating information will be synchronized to the entire consulting service network, forming a continuously optimized multi-party collaborative trust system.

9. The blockchain-based enterprise information consulting service security verification platform according to claim 8, characterized in that: The record storage and optimization module includes: Blockchain technology is used to store the collaboration history in an immutable manner. Trust records are then obtained from the distributed ledger to verify their unique identifier and integrity. If the verification is successful, the interaction data in the collaboration history is extracted to determine the basic information for trust rating. Based on the collaborative behavior of the participants in the interaction data analysis, the trust rating is calculated using the support vector machine algorithm, and the updated trust rating information is generated. The updated trust rating information is then synchronized to each node of the consulting service network through a smart contract to obtain consistent trust data across the entire network. The system obtains synchronized trust data from the consulting service network, analyzes the dynamic optimization trend of multi-party collaboration, uses the Naive Bayes algorithm to predict collaboration efficiency, determines the optimization direction, and adjusts the storage strategy of collaboration history based on the optimization direction. If the trust rating is higher than the preset threshold, high-trust interaction data is stored first, a simplified collaboration record is generated, and the simplified collaboration record is verified through a distributed ledger. If the verification is successful, the collaboration rules of the consulting service network are updated to obtain an optimized multi-party collaboration trust system.

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