Multi-source data isolation method and system based on privacy computing platform

Abstract

The invention discloses a multi-source data isolation method and system based on a privacy computing platform, and relates to the technical field of data isolation. After basic parameters and transmission parameters of platform multi-source data are obtained, the basic parameters and the transmission parameters are comprehensively analyzed based on an isolation rule to generate an isolation value for each piece of data; whether the data needs to be isolated or not is judged according to a comparison result of an isolation value and an isolation threshold value, after all multi-source data are analyzed, an unisolated data set and an isolated data set are generated, the isolated data set is divided into public information, sensitive information and privacy information, a dynamic isolation strategy is appointed according to a hierarchical division result of the isolated data set, and a dynamic isolation result is obtained. And adjusting the isolation level according to the real-time privacy demand and the data flow direction. According to the isolation method, after the multi-source data is divided into the non-isolated data set and the isolated data set, the data in the isolated data set is dynamically isolated, so that the analysis and processing efficiency of the multi-source data is improved, and the isolation effect is better.

Description

Technical Field

[0001] The present invention relates to the technical field of data isolation, and particularly to a multi-source data isolation method and system based on a privacy computing platform. Background Art

[0002] A multi-source data isolation system is a system for managing and maintaining multi-source data, whose goal is to ensure the isolation between various data sources to protect the security, integrity, and availability of data. Such a system usually plays an important role in organizations involving different departments, business units, or data providers, such as large enterprises, government agencies, or various dispersed data sources within an organization;

[0003] The existing technology has the following deficiencies: When isolating multi-source data, the existing isolation methods only consider the privacy and sensitivity of multi-source data before isolation. However, in actual applications, multi-source data is also affected by security factors. Adopting the above isolation methods will not only reduce the isolation effect on multi-source data but also reduce the processing efficiency of multi-source data. Summary of the Invention

[0004] The purpose of the present invention is to provide a multi-source data isolation method and system based on a privacy computing platform to solve the deficiencies in the background art.

[0005] To achieve the above purpose, the present invention provides the following technical solution: A multi-source data isolation method based on a privacy computing platform, the isolation method comprising the following steps:

[0006] After obtaining the basic parameters and transmission parameters of the platform multi-source data, comprehensively analyze the basic parameters and transmission parameters based on the isolation rules to generate an isolation value for each data;

[0007] Judge whether data needs to be isolated based on the comparison result between the isolation value and the isolation threshold. After analyzing all multi-source data, generate an unisolated data set and an isolated data set;

[0008] Divide the isolated data set into public information, sensitive information, and privacy information, and specify a dynamic isolation strategy according to the hierarchical division result of the isolated data set;

[0009] Adjust the isolation level according to real-time privacy requirements and data flow directions, and select different encryption algorithms and key management schemes according to the sensitivity and importance of the data;

[0010] Dynamically adjust the degree of desensitization according to the context and relevance of the data, and implement distributed identity authentication and permission management through smart contract and blockchain technologies.

[0011] Preferably, obtain the basic parameters and transmission parameters of the platform's multi-source data. The basic parameters include the complexity of permissions and the proportion of public data, and the transmission parameters include the encryption strength and the data security warning frequency.

[0012] Preferably, after obtaining the basic parameters and transmission parameters of the platform's multi-source data, comprehensively analyze the basic parameters and transmission parameters based on the isolation rules to generate an isolation value for each data, including the following steps:

[0013] Analyze the complexity of permissions, the proportion of public data, the encryption strength, and the data security warning frequency obtained in real time for the platform's multi-source data through the isolation rules to obtain the isolation value. The expression is:

[0014]

[0015] Transmission parameters, α and β are the proportionality coefficients of the basic parameters and transmission parameters respectively, and both α and β are greater than 0. N is the number of data sources, C is the number of access controls for different categories or levels of each data source, P is the percentage of sensitive data in the data source, U is the number of users or user groups, sj_public is the amount of public data, sj_total is the total amount of data, jmq is the encryption strength, cs is the number of security warnings, and ΔT is the monitoring duration.

[0016] Preferably, judge whether the data needs to be isolated based on the comparison result between the isolation value and the isolation threshold. After analyzing all the multi-source data, generate an unisolated data set and an isolated data set, including the following steps:

[0017] After obtaining the isolation value of the platform's multi-source data, compare the isolation value of the multi-source data with the isolation threshold. The isolation threshold is used to judge whether the multi-source data needs to be isolated. If the isolation value is greater than the isolation threshold, judge that the multi-source data needs to be isolated. If the isolation value is less than or equal to the isolation threshold, judge that the multi-source data does not need to be isolated. Establish an isolated data set for the multi-source data that needs to be isolated, and establish an unisolated data set for the multi-source data that does not need to be isolated.

[0018] Preferably, divide the isolated data set into public information, sensitive information, and private information, and specify a dynamic isolation strategy based on the hierarchical division result of the isolated data set, including the following steps:

[0019] Classify the data set, clearly define public information, sensitive information, and private information, mark each data type, define different isolation levels for each data classification, allow the isolation level to be adjusted according to real-time privacy requirements and data flow directions, deploy a real-time monitoring system, monitor changes in data flow directions and privacy requirements, and adjust the isolation strategy in a timely manner according to the monitoring results.

[0020] Preferably, different encryption algorithms and key management schemes are selected according to the sensitivity and importance of the data, including the following steps:

[0021] Classify the data, divide it into different levels according to sensitivity and importance. For highly sensitive and important data, select the AES-256 algorithm. For public information, use the lightweight encryption AES-128 algorithm. According to the sensitivity of each data level, formulate different key lifecycle management strategies, use a hardware security module to store and manage keys, and use a random and secure initialization vector.

[0022] Preferably, the desensitization degree is dynamically adjusted according to the context and relevance of the data, including the following steps:

[0023] Analyze the context of the data, understand the sensitivity of the data in different environments and uses, evaluate the relevance between data, determine the relationship between data, formulate privacy metric indicators to quantify the privacy degree of the data, classify the data, and define different desensitization strategies according to different categories and sensitivities.

[0024] Preferably, distributed authentication and permission management are implemented through smart contracts and blockchain technology to ensure that data can only be accessed by authorized parties, including the following steps:

[0025] Clearly define the identities and permissions in the system, including users, organizations, or other participating parties, and determine the operations performed by each identity and the resources accessed. Deploy a blockchain network, select a blockchain platform to build a distributed network, define the authentication process, permission allocation rules, and data access control policies. Users or participating parties register their identities on the blockchain, and each identity obtains a unique identifier for authentication in the smart contract. Use smart contracts to perform permission allocation on the blockchain, determine which identities have access to which resources, and set the corresponding permission levels. Before accessing resources, users or participating parties need to go through the identity authentication process on the blockchain, including digital signatures and multi-factor authentication methods. When a user requests access to resources, the smart contract performs identity authentication and permission checks.

[0026] A multi-source data isolation system based on a privacy computing platform, including a collection module, an analysis module, a comparison module, a hierarchical division module, a key management module, a desensitization module, and an access control module;

[0027] Collection module: Obtain the basic parameters and transmission parameters of the multi-source data of the platform;

[0028] Analysis module: Based on the isolation rules, comprehensively analyze the basic parameters and transmission parameters to generate an isolation value for each data;

[0029] Comparison module: Determine whether data needs to be isolated based on the comparison result between the isolation value and the isolation threshold. After analyzing all multi-source data, generate an unisolated data set and an isolated data set;

[0030] Hierarchical division module: Divide the isolated data set into public information, sensitive information, and privacy information, and specify a dynamic isolation policy based on the hierarchical division result of the isolated data set, and adjust the isolation level according to real-time privacy requirements and data flow;

[0031] Key management module: Select different encryption algorithms and key management schemes according to the sensitivity and importance of the data;

[0032] Desensitization module: Dynamically adjust the desensitization degree according to the context and relevance of the data;

[0033] Access control module: Implement distributed identity authentication and permission management through smart contract and blockchain technologies.

[0034] In the above technical solutions, the technical effects and advantages provided by the present invention are as follows:

[0035] 1. After obtaining the basic parameters and transmission parameters of the multi-source data on the platform, the present invention comprehensively analyzes the basic parameters and transmission parameters based on the isolation rules to generate an isolation value for each data. Determine whether data needs to be isolated based on the comparison result between the isolation value and the isolation threshold. After analyzing all multi-source data, generate an unisolated data set and an isolated data set, divide the isolated data set into public information, sensitive information, and privacy information, and specify a dynamic isolation policy based on the hierarchical division result of the isolated data set, and adjust the isolation level according to real-time privacy requirements and data flow. After dividing the multi-source data into an unisolated data set and an isolated data set, the isolation method then performs dynamic isolation on the data in the isolated data set, which not only improves the analysis and processing efficiency of multi-source data, but also has a better isolation effect;

[0036] 2. By obtaining the basic parameters and transmission parameters of the multi-source data on the platform, the basic parameters include permission complexity and public data ratio, and the transmission parameters include encryption strength and data security warning frequency. The permission complexity, public data ratio, encryption strength, and data security warning frequency obtained in real time for the multi-source data on the platform are analyzed through isolation rules to obtain an isolation value, so as to analyze whether multi-source data needs to be isolated from multiple dimensions, and the analysis is more accurate. Description of the drawings

[0037] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0038] Figure 1 It is the flowchart of the method of the present invention. Specific embodiments

[0039] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0040] Embodiment 1: Please refer to Figure 1 As shown, a multi-source data isolation method based on a privacy computing platform in this embodiment, the isolation method includes the following steps:

[0041] After obtaining the basic parameters and transmission parameters of the platform multi-source data, comprehensively analyze the basic parameters and transmission parameters based on the isolation rules to generate an isolation value for each data. Determine whether data needs to be isolated based on the comparison result between the isolation value and the isolation threshold. After analyzing all multi-source data, generate an unisolated data set and an isolated data set. Classify the isolated data set into public information, sensitive information, and private information, and specify a dynamic isolation policy according to the hierarchical division result of the isolated data set. Adjust the isolation level according to real-time privacy requirements and data flow directions. For example, increase the isolation intensity during sensitive data processing, and reduce the isolation level when privacy information is not involved to improve efficiency. Select different encryption algorithms and key management schemes according to the sensitivity and importance of the data. Adopt stronger encryption means for highly sensitive data and use lightweight encryption for public information to improve performance. Dynamically adjust the desensitization degree according to the context and relevance of the data, which can ensure the usefulness of the data while protecting privacy. Implement distributed identity authentication and permission management through smart contracts and blockchain technology to ensure that data can only be accessed by authorized parties.

[0042] After obtaining the basic parameters and transmission parameters of the platform's multi-source data, based on the isolation rules, comprehensively analyze the basic parameters and transmission parameters to generate an isolation value for each data. Determine whether the data needs to be isolated based on the comparison result between the isolation value and the isolation threshold. After analyzing all the multi-source data, generate an unisolated data set and an isolated data set. Divide the isolated data set into public information, sensitive information, and private information, and specify a dynamic isolation policy according to the hierarchical division result of the isolated data set. Adjust the isolation level according to the real-time privacy requirements and data flow. After dividing the multi-source data into an unisolated data set and an isolated data set, this isolation method further performs dynamic isolation on the data in the isolated data set, which not only improves the analysis and processing efficiency of the multi-source data, but also has a better isolation effect.

[0043] Embodiment 2: After obtaining the basic parameters and transmission parameters of the platform's multi-source data, based on the isolation rules, comprehensively analyze the basic parameters and transmission parameters to generate an isolation value for each data, including the following steps:

[0044] Obtain the basic parameters and transmission parameters of the platform's multi-source data. The basic parameters include the permission complexity and the proportion of public data, and the transmission parameters include the encryption strength and the data security warning frequency;

[0045] Analyze the permission complexity, proportion of public data, encryption strength, and data security warning frequency obtained in real time for the platform's multi-source data through the isolation rules to obtain the isolation value. The expression is:

[0046]

[0047] Transmission parameters, α and β are the proportionality coefficients of the basic parameters and transmission parameters respectively, and both α and β are greater than 0. N is the number of data sources, C is the number of access controls for different categories or levels of each data source, P is the percentage of sensitive data in the data source, U is the number of users or user groups, sj_public is the amount of public data, sj_total is the total amount of data, jmq is the encryption strength, cs is the number of security warnings, and ΔT is the monitoring duration.

[0048] This application obtains the basic parameters and transmission parameters of the platform's multi-source data. The basic parameters include the permission complexity and the proportion of public data, and the transmission parameters include the encryption strength and the data security warning frequency. Analyze the permission complexity, proportion of public data, encryption strength, and data security warning frequency obtained in real time for the platform's multi-source data through the isolation rules to obtain the isolation value, so as to analyze whether the multi-source data needs to be isolated from multiple dimensions, and the analysis is more accurate.

[0049] Permission complexity:

[0050] Data source investigation: By communicating with data owners or administrators, understand the access permission settings for each data source, including how many users have access permissions.

[0051] Access log analysis: Analyze access logs to identify the number of users with different permission levels and calculate the permission complexity.

[0052] The greater the permission complexity, the more necessary it is to isolate multi-source data. Specifically:

[0053] Potential data leakage risks:

[0054] Reason: High permission complexity may lead to lax permission control, and users may be able to access sensitive information that they do not need to know or process.

[0055] Measures: For situations with high permission complexity, implement more stringent access control and permission management to ensure that only authorized personnel can access specific data.

[0056] Privacy leakage risks:

[0057] Reason: More users mean more potential privacy leakage risks, especially when users have access to sensitive information.

[0058] Measures: Implement privacy protection measures such as data masking and encryption to reduce the privacy leakage risks caused by user permission complexity.

[0059] Complex access control management:

[0060] Reason: High permission complexity makes access control management more cumbersome and prone to errors or omissions.

[0061] Measures: Automate access control management and use authentication and authorization tools to reduce human errors and improve the overall reliability of the system.

[0062] Increased security requirements:

[0063] Reason: Higher permission complexity is usually accompanied by higher requirements for data security because more users mean more potential threats.

[0064] Measures: Strengthen security measures for data transmission and storage, and use powerful encryption algorithms, secure transmission protocols, etc. to ensure the security of data during transmission and storage.

[0065] Compliance requirements:

[0066] Reason: In some industries or regulations, the permission requirements for highly sensitive data may be more stringent.

[0067] Measures: Ensure that data processing complies with relevant regulations and industry standards, conduct compliance audits, and strictly supervise and review data under high privilege complexity.

[0068] Proportion of publicly available data:

[0069] Data classification and sampling: Classify multi-source data and estimate the proportion of data in each category through sampling.

[0070] Sensitivity assessment: Evaluate sensitive data to determine its proportion in the total data;

[0071] The smaller the proportion of publicly available data in multi-source data, the more private data there is in multi-source data, and the more isolation is required. Specifically:

[0072] Increased risk of leakage due to high proportion of highly private data:

[0073] Reason: The larger the proportion of publicly available data, the more sensitive information the system contains, increasing the potential risk of being accessed by unauthorized users or malicious behavior.

[0074] Measures: Strengthen access control, restrict access to private data to only authorized users, and use technologies such as encryption and desensitization to reduce the risk of privacy leakage.

[0075] Privacy regulations and compliance requirements:

[0076] Reason: A high proportion of publicly available data may mean more stringent regulations and compliance requirements, setting higher standards for the legitimate use and protection of private data.

[0077] Measures: Comply with relevant regulations, ensure that data processing complies with regulations and industry standards, conduct privacy impact assessments, and develop compliance strategies.

[0078] Improve user trust:

[0079] Reason: Users are very concerned about the protection of their private data, and a high proportion of publicly available data may reduce users' trust in the system.

[0080] Measures: Take proactive privacy protection measures, such as transparent privacy policies, user education and communication, to improve users' trust in data privacy security.

[0081] Increased potential for abuse:

[0082] Reason: More private data means a greater potential for abuse, and improper data access may lead to information leakage or abuse.

[0083] Measures: Strengthen the supervision and audit mechanism, conduct real-time monitoring of data access, establish an abuse detection system, and promptly detect and respond to improper data access behaviors.

[0084] Increased responsibility of data owners:

[0085] Reason: More privacy data may increase the responsibility of data owners or administrators, and these data need to be managed and protected more carefully.

[0086] Measures: Provide training and guidance to ensure that data owners and administrators understand the best practices of privacy protection and enhance their awareness of responsibility.

[0087] Encryption strength:

[0088] Encryption configuration check: Check the encryption configuration during data transmission and storage, and obtain the encryption algorithms and key lengths used.

[0089] Security audit: Conduct a security audit to evaluate the strength of encryption algorithms and their compliance with best practices.

[0090] The greater the encryption strength of multi-source data, the more isolation is required, specifically:

[0091] Improve data security requirements:

[0092] Reason: High encryption strength usually reflects higher expectations for data security, and the system may be handling more sensitive and important information.

[0093] Measures: Implement more stringent isolation strategies for data with high encryption strength to ensure that only authorized users can access and process this highly secure data.

[0094] Reduce the risk of potential encryption attacks:

[0095] Reason: High encryption strength reduces the risk of potential encryption attacks, but once the encryption is cracked, the threat is higher.

[0096] Measures: Isolation can help limit the access scope of potential attackers to the system. Even if the encryption is cracked, it is difficult to obtain the sensitive data of the entire system.

[0097] Address unknown encryption vulnerabilities:

[0098] Reason: Although high encryption strength can prevent known attack methods, there may be unknown encryption vulnerabilities or backdoors.

[0099] Measures: Isolation can provide an additional line of defense. Even if a part of the system is breached, it is difficult to penetrate the entire system, reducing the risk of unknown vulnerabilities to the system.

[0100] Address key management issues:

[0101] Reason: High encryption strength usually requires more powerful and complex key management, and key management issues may affect the security of the entire system.

[0102] Measures: Isolation can help simplify key management, isolate different encryption modules, reduce the complexity of key management, and improve the overall security of the system.

[0103] Improving the overall security of the system:

[0104] Reason: The improvement of encryption strength is part of the improvement of the overall system security, and isolation can enhance the overall security at the system level.

[0105] Measures: By implementing physical isolation, network isolation and other means, ensure that even when threatened at the encryption level, attackers are still difficult to penetrate into other system components.

[0106] Frequency of data security warnings:

[0107] Security event logs: Analyze security event logs to understand security events and warnings that occurred over a period of time.

[0108] Anomaly detection system: Use an anomaly detection system to monitor data access and transmission to obtain the frequency of abnormal events.

[0109] The higher the frequency of data security warnings from multiple sources, the more isolation is required, specifically:

[0110] Potential security threats:

[0111] Reason: Frequent security warnings may imply potential security threats in the system, which may be malicious attacks, abnormal behaviors or other security risks.

[0112] Measures: Strengthening isolation can help prevent potential threats from spreading to the entire system and limit the impact of threats on the system.

[0113] Improving the overall security of the system:

[0114] Reason: Frequent security warnings may indicate insufficient security of the entire system, and the overall system security needs to be improved.

[0115] Measures: By isolating different system components, limit the spread range of potential threats and improve the overall security of the system.

[0116] Preventing the spread of attacks:

[0117] Reason: If frequent security warnings come from attacks or abnormal activities, isolation can prevent attacks from spreading within the system and reduce the scope of damage.

[0118] Measures: Isolate critical components to ensure that even if a part of the system is attacked, it is difficult for attackers to further penetrate into other critical systems.

[0119] Improve response speed:

[0120] Reason: Rapid response to security alerts is crucial for preventing significant damage caused by potential threats, and isolation can enhance the effectiveness of rapid response.

[0121] Measures: Design a rapid response mechanism, including automatically isolating threatened components to reduce response time.

[0122] Reduce the risk of data leakage:

[0123] Reason: Security alerts may be related to potential data leakage risks. Isolation can reduce the leakage risk and limit the spread of data within the system.

[0124] Measures: Implement data isolation, restrict the access scope of sensitive information, and reduce the leakage risk.

[0125] Improve system fault tolerance:

[0126] Reason: In the case of frequent security alerts, isolation can improve the fault tolerance of the system, ensuring that even if a part of the system is attacked or crashes, the overall system can still operate normally.

[0127] Measures: Design a flexible system architecture that allows other parts to continue operating while isolating a part of the system.

[0128] Judge whether data needs to be isolated based on the comparison result between the isolation value and the isolation threshold. After analyzing all multi-source data, generate an unisolated data set and an isolated data set, including the following steps:

[0129] The larger the isolation value, the more necessary it is to isolate the multi-source data. After obtaining the isolation value of the platform multi-source data, compare the isolation value of the multi-source data with the isolation threshold. The isolation threshold is used to judge whether the multi-source data needs to be isolated. If the isolation value is greater than the isolation threshold, judge that the multi-source data needs to be isolated. If the isolation value is less than or equal to the isolation threshold, judge that the multi-source data does not need to be isolated. Establish an isolated data set for the multi-source data that needs to be isolated, and establish an unisolated data set for the multi-source data that does not need to be isolated.

[0130] Divide the isolated data set into public information, sensitive information, and privacy information, and specify a dynamic isolation strategy based on the hierarchical division result of the isolated data set. Adjust the isolation level according to real-time privacy requirements and data flow. For example, increase the isolation intensity during sensitive data processing, and can reduce the isolation level to improve efficiency when privacy information is not involved, including the following steps:

[0131] Data Classification and Tagging: Classify the dataset and clearly define public information, sensitive information, and privacy information. Tag each data type to ensure a clear understanding of the sensitivity level and privacy level of each data item.

[0132] Isolation Level Definition: Define different isolation levels for each data classification. For example, it can be divided into three levels: low, medium, and high, representing different privacy levels. Public information can be set to a level that does not require isolation.

[0133] Dynamic Isolation Policy Design: Design a dynamic isolation policy that allows adjusting the isolation level according to real-time privacy requirements and data flow. Consider the following factors:

[0134] Privacy Requirements: Determine the current privacy requirements based on privacy policies and regulations.

[0135] Data Flow: Consider where the data comes from and where it will flow to determine the appropriate isolation level.

[0136] Data Usage Scenarios: Dynamically adjust the isolation level according to specific business scenarios.

[0137] Real-time Monitoring and Feedback Mechanism: Deploy a real-time monitoring system to monitor changes in data flow and privacy requirements. Establish a feedback mechanism to enable the system to adjust the isolation level in real-time.

[0138] Isolation Policy Adjustment: Adjust the isolation policy in a timely manner based on the monitoring results. For example, when processing sensitive data, the isolation intensity can be increased, and when privacy information is not involved, the isolation level can be reduced to improve efficiency.

[0139] Employee Training and Awareness Enhancement: Provide employee training to strengthen their understanding of privacy policies and dynamic isolation policies. Establish employees' privacy protection awareness.

[0140] Compliance Review and Continuous Improvement: Conduct regular compliance reviews to ensure that the isolation policy complies with relevant regulations and policies. Continuously improve the policy to adapt to changing privacy requirements and business environments.

[0141] Select different encryption algorithms and key management schemes according to the sensitivity and importance of the data. Adopt stronger encryption means for highly sensitive data and use lightweight encryption for public information to improve performance, including the following steps:

[0142] Sensitivity and Importance Classification: Classify the data and divide it into different levels according to sensitivity and importance. Clearly define the security requirements for each level.

[0143] Encryption Algorithm Selection: For highly sensitive and important data, select a more powerful and secure encryption algorithm, such as AES-256. For public information, consider using a lightweight encryption algorithm, such as AES-128 or a faster algorithm to improve performance.

[0144] Key Length Selection: For highly sensitive data, select a longer key length to increase the difficulty of cracking. For example, when using AES, for high-level data, a 256-bit key length can be selected, while for low-sensitivity data, a 128-bit key length can be selected.

[0145] Key Lifecycle Management: Based on the sensitivity level of each data level, formulate different key lifecycle management strategies. For highly sensitive data, keys may need to be updated more frequently.

[0146] Use of Hardware Security Module (HSM): For highly sensitive data, consider using a hardware security module (HSM) to store and manage keys to improve key security.

[0147] Randomness and Initialization Vector: For high-level data, ensure the use of sufficient randomness and a secure initialization vector to increase the strength and security of encryption.

[0148] Performance and Resource Consumption Evaluation: Evaluate the impact of different encryption algorithms and key lengths on system performance and resource consumption. Ensure that the selected encryption scheme can meet performance requirements in practical applications.

[0149] Compliance Considerations: Ensure that the selected encryption algorithms and key management schemes comply with applicable regulations and compliance standards, such as FIPS, HIPAA, etc.

[0150] Dynamically adjust the degree of data masking according to the context and relevance of the data, which can ensure the usefulness of the data while protecting privacy, including the following steps:

[0151] Context Analysis: Analyze the context of the data to understand the sensitivity of the data in different environments and uses. Consider the actual application scenarios of the data to better understand the appropriate degree of data masking.

[0152] Relevance Assessment: Evaluate the relevance between data. Determine the relationship between data to adjust the degree of data masking according to relevance. Data with high relevance may require more detailed data masking.

[0153] Privacy Metric Indicators: Develop privacy metric indicators to quantify the privacy level of data. Consider using indicators such as k-anonymity, l-diversity, t-closeness, etc. to help evaluate the privacy protection effect.

[0154] Data Classification: Classify the data and define different data masking strategies according to different categories and sensitivities. For example, more stringent data masking methods may be adopted for personal identity information.

[0155] Dynamic Data Masking Strategy Design: Design a dynamic data masking strategy that allows for dynamic adjustment of the masking level based on context and relevance. This may involve the flexible application of different data masking algorithms and parameters for different data.

[0156] Application of Machine Learning and Intelligent Algorithms: Utilize machine learning and intelligent algorithms to automatically adjust the masking level through learning and analysis of the data. This can improve the system's adaptability and intelligence.

[0157] Real-time Monitoring and Feedback Mechanism: Deploy a real-time monitoring system to monitor data usage and changes in privacy requirements. Establish a feedback mechanism to enable the system to adjust the masking level in real-time.

[0158] Risk Assessment: Conduct regular risk assessments to analyze the privacy risks of the data. Based on the assessment results, adjust the data masking strategy to address new privacy threats.

[0159] Compliance Considerations: Ensure that the designed data masking strategy complies with applicable regulations and compliance standards, such as GDPR, HIPAA, etc.

[0160] Implement distributed authentication and permission management through smart contracts and blockchain technology to ensure that data can only be accessed by authorized parties, including the following steps:

[0161] Define Identities and Permissions: Clearly define the identities and permissions in the system, including users, organizations, or other parties involved, and determine the operations that each identity can perform and the resources that can be accessed.

[0162] Build a Blockchain Network: Deploy a blockchain network and select an appropriate blockchain platform (e.g., Ethereum, Hyperledger Fabric, etc.) to build a distributed network. Ensure that the network can provide support for smart contracts.

[0163] Smart Contract Design: Design smart contracts, including the logic for identity authentication and permission management. Smart contracts can define the identity authentication process, permission allocation rules, and data access control policies.

[0164] Identity Registration: Users or parties involved register their identities on the blockchain. Each identity will be assigned a unique identifier for identity authentication in the smart contract.

[0165] Permission Allocation: Use smart contracts to allocate permissions on the blockchain. Determine which identities have access to which resources and set the corresponding permission levels.

[0166] Identity authentication process: Before accessing resources, users or participants need to go through an identity authentication process on the blockchain. This can include methods such as digital signatures and multi-factor authentication.

[0167] Smart contract execution: When a user requests access to a resource, the smart contract performs identity authentication and permission checks. Only identities that have passed authentication and have sufficient permissions can access the corresponding resources.

[0168] Access auditing and traceability: The immutable and traceable characteristics of the blockchain can be used to record events of each access and permission change, enabling access auditing and ensuring data security.

[0169] Dynamic permission adjustment: Dynamic permission adjustment can be achieved through smart contracts, and permissions can be updated in real time according to actual needs and business changes.

[0170] Compliance and encryption guarantee: Consider compliance issues to ensure that smart contracts and blockchain networks comply with relevant regulations. At the same time, use encryption technology to ensure the security of data during transmission and storage.

[0171] Monitoring and alarming: Set up a monitoring mechanism to monitor the running status of the blockchain network and smart contracts in real time, set alarm rules, and detect potential problems in a timely manner.

[0172] Embodiment 3: A multi-source data isolation system based on a privacy computing platform described in this embodiment includes a collection module, an analysis module, a comparison module, a hierarchical division module, a key management module, a desensitization module, and an access control module;

[0173] Collection module: Obtain the basic parameters and transmission parameters of the multi-source data of the platform;

[0174] Analysis module: Based on the isolation rules, comprehensively analyze the basic parameters and transmission parameters to generate an isolation value for each data;

[0175] Comparison module: Judge whether data needs to be isolated based on the comparison result of the isolation value and the isolation threshold. After analyzing all multi-source data, generate an unisolated data set and an isolated data set;

[0176] Hierarchical division module: Divide the isolated data set into public information, sensitive information, and privacy information, and specify a dynamic isolation strategy based on the hierarchical division result of the isolated data set. Adjust the isolation level according to real-time privacy requirements and data flow. For example, increase the isolation intensity during sensitive data processing, and reduce the isolation level when privacy information is not involved to improve efficiency;

[0177] Key Management Module: Select different encryption algorithms and key management schemes according to the sensitivity and importance of data, adopt stronger encryption means for highly sensitive data, and use lightweight encryption for public information to improve performance;

[0178] Data Masking Module: Dynamically adjust the degree of data masking according to the context and relevance of the data, which can ensure the usefulness of the data while protecting privacy;

[0179] Access Control Module: Implement distributed authentication and permission management through smart contracts and blockchain technology to ensure that data can only be accessed by authorized parties.

[0180] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to obtain a formula that is closest to the actual situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0181] In the description of this specification, the descriptions referring to terms such as "one embodiment", "example", "specific example", etc. mean that the specific features, structures, materials or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described can be combined in a suitable manner in any one or more embodiments or examples.

[0182] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the present invention to only the specific implementation manners. Obviously, according to the content of this specification, many modifications and variations can be made. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art in the relevant technical field can well understand and utilize the present invention. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A multi-source data isolation method based on a privacy computing platform, characterized by: The isolation method comprises the following steps: After obtaining the basic parameters and transmission parameters of multi-source data on the platform, the basic parameters and transmission parameters are comprehensively analyzed based on the isolation rules to generate isolation values ​​for each data; By comparing the isolation value with the isolation threshold, it is determined whether the data needs to be isolated. After analyzing all multi-source data, an unisolated data set and an isolated data set are generated. The isolated data set is divided into public information, sensitive information and private information, and a dynamic isolation strategy is specified based on the hierarchical division results of the isolated data set; Adjust the isolation level according to real-time privacy requirements and data flow, and select different encryption algorithms and key management solutions based on the sensitivity and importance of the data; The degree of anonymization is dynamically adjusted according to the context and relevance of the data, and distributed identity authentication and permission management are achieved through smart contracts and blockchain technology.

2. According to claim 1, a multi-source data isolation method based on a privacy computing platform is characterized in that: Obtain the basic parameters and transmission parameters of the platform's multi-source data. The basic parameters include permission complexity and the proportion of public data. The transmission parameters include encryption strength and data security warning frequency.

3. The multi-source data isolation method based on the privacy computing platform according to claim 2 is characterized in that: After obtaining the basic parameters and transmission parameters of the platform's multi-source data, the basic parameters and transmission parameters are comprehensively analyzed based on the isolation rules to generate isolation values ​​for each data, including the following steps: The isolation value is obtained by analyzing the complexity of permissions, the proportion of public data, the encryption strength, and the frequency of data security warnings for real-time acquisition of multi-source data on the platform through isolation rules. The expression is: Transmission parameters, α and β are the proportional coefficients of the basic parameters and transmission parameters respectively, and α and β are both greater than 0, N is the number of data sources, C is the number of different categories or levels of access control for each data source, P is the percentage of sensitive data in the data source, U is the number of users or user groups, sj public is the amount of public data, sj total is the total amount of data, jmq is the encryption strength, cs is the number of security warnings, and ΔT is the monitoring duration.

4. The multi-source data isolation method based on the privacy computing platform according to claim 3 is characterized in that: The comparison result between the isolation value and the isolation threshold determines whether the data needs to be isolated. After analyzing all multi-source data, an unisolated data set and an isolated data set are generated, including the following steps: After obtaining the isolation value of the platform's multi-source data, compare the isolation value of the multi-source data with the isolation threshold. The isolation threshold is used to determine whether the multi-source data needs to be isolated. If the isolation value is greater than the isolation threshold, it is determined that the multi-source data needs to be isolated. If the isolation value is less than or equal to the isolation threshold, it is determined that the multi-source data does not need to be isolated. An isolated data set is established for the multi-source data that needs to be isolated, and a non-isolated data set is established for the multi-source data that does not need to be isolated.

5. The multi-source data isolation method based on the privacy computing platform according to claim 4 is characterized in that: Dividing the isolated data set into public information, sensitive information, and private information, and specifying a dynamic isolation strategy based on the hierarchical division results of the isolated data set includes the following steps: Classify data sets, clearly define public information, sensitive information, and private information, label each data type, define different isolation levels for each data classification, allow isolation levels to be adjusted according to real-time privacy needs and data flows, deploy a real-time monitoring system, monitor changes in data flows and privacy needs, and adjust isolation strategies in a timely manner based on monitoring results.

6. The multi-source data isolation method based on the privacy computing platform according to claim 5 is characterized in that: Select different encryption algorithms and key management solutions based on the sensitivity and importance of the data, including the following steps: Classify the data and divide it into different levels according to its sensitivity and importance. For highly sensitive and important data, select the AES-256 algorithm. For public information, use the lightweight encryption AES-128 algorithm. According to the sensitivity of each data level, formulate different key lifecycle management strategies, use hardware security modules to store and manage keys, and use random and secure initialization vectors.

7. The multi-source data isolation method based on the privacy computing platform according to claim 6 is characterized in that: Dynamically adjust the level of desensitization based on the context and relevance of the data, including the following steps: Analyze the context of the data, understand the sensitivity of the data in different environments and for different purposes, evaluate the correlation between the data, determine the relationship between the data, develop privacy metrics to quantify the privacy level of the data, classify the data, and define different desensitization strategies based on different categories and sensitivities.

8. The multi-source data isolation method based on the privacy computing platform according to claim 7 is characterized in that: Distributed identity authentication and permission management are implemented through smart contracts and blockchain technology to ensure that data can only be accessed by authorized parties, including the following steps: Clearly define the identities and permissions in the system, including users, organizations, or other participants, and determine the operations performed and resources accessed by each identity, deploy the blockchain network, select a blockchain platform to build a distributed network, define the identity authentication process, permission allocation rules, and data access control policies, and users or participants register their identities on the blockchain. Each identity obtains a unique identifier for identity authentication in smart contracts. Use smart contracts to allocate permissions on the blockchain, determine which identities have access to which resources, and set corresponding permission levels. Before accessing resources, users or participants need to go through the identity authentication process on the blockchain, including digital signatures and multi-factor authentication methods. When users request access to resources, smart contracts perform identity authentication and permission checks.

9. A multi-source data isolation system based on a privacy computing platform, used to implement the isolation method according to any one of claims 1 to 8, characterized in that: It includes acquisition module, analysis module, comparison module, hierarchical division module, key management module, desensitization module and access control module; Acquisition module: obtains basic parameters and transmission parameters of multi-source data on the platform; Analysis module: Based on the isolation rules, it comprehensively analyzes the basic parameters and transmission parameters to generate isolation values ​​for each data; Comparison module: determines whether data needs to be isolated by comparing the isolation value with the isolation threshold. After analyzing all multi-source data, it generates non-isolated data sets and isolated data sets. Hierarchical division module: divides the isolated data set into public information, sensitive information and private information, and specifies dynamic isolation strategies based on the hierarchical division results of the isolated data set, and adjusts the isolation level according to real-time privacy requirements and data flow; Key management module: select different encryption algorithms and key management schemes according to the sensitivity and importance of the data; Desensitization module: dynamically adjusts the degree of desensitization based on the context and relevance of the data; Access control module: realize distributed identity authentication and permission management through smart contracts and blockchain technology.

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