Cross-industry data sharing methods and systems utilizing trusted data spaces

Abstract

This application discloses a method and system for cross-industry data sharing using a trusted data space, relating to the field of data sharing technology. The method includes: obtaining cross-industry data dimensional relationships; obtaining sharing requests and performing dimensional migration analysis on the sharing requests to obtain a set of dimensional migration paths; performing an adaptive reorganization operation on the data to be shared based on the dimensional migration path set to obtain reorganized shared data; conducting a cross-industry confidence assessment of the reorganized shared data based on the cross-industry data dimensional relationships and the dimensional migration path set to generate a sharing decision result; and performing data sharing on the reorganized shared data through a secure channel in the trusted data space. This solves the technical problems of difficulties in cross-industry data sharing and waste of data resources in existing systems.

Description

Technical Field

[0001] This application relates to the field of data sharing technology, specifically to cross-industry data sharing methods and systems utilizing trusted data spaces. Background Technology

[0002] With the advent of the digital age, the amount of data accumulated across industries has exploded. While data from different industries holds immense value, cross-industry data sharing faces numerous challenges due to industry barriers, data security, and privacy protection issues. Under traditional data sharing models, differences in data formats, standards, and semantics across industries make data integration and fusion difficult. Furthermore, data is often confined within its own industry, hindering effective cross-industry circulation and integration, leading to a waste of data resources. Summary of the Invention

[0003] This application provides a method and system for cross-industry data sharing that utilizes a trusted data space, thereby solving the technical problems of difficulties in cross-industry data sharing and waste of data resources.

[0004] The technical solution to the above-mentioned technical problems in this application is as follows:

[0005] In a first aspect, this application provides a method for cross-industry data sharing utilizing a trusted data space, the method comprising:

[0006] Based on the target cross-industry data space, we traverse multiple industries to identify data dimensions and obtain cross-industry data dimension relationships.

[0007] Obtain the sharing request, and perform dimension migration analysis on the sharing request based on the cross-industry data dimension relationship to obtain the dimension migration path set;

[0008] Based on the set of dimensional migration paths, perform an adaptive reorganization operation on the data to be shared to obtain the reorganized shared data;

[0009] Based on the cross-industry data dimension relationships and the dimension migration path set, a cross-industry confidence assessment is performed on the reorganized shared data, and a sharing decision result is generated based on the cross-industry confidence assessment results.

[0010] In response to the shared decision result, data sharing is performed on the recombined shared data through a secure channel in the trusted data space.

[0011] Secondly, this application provides a cross-industry data sharing system utilizing a trusted data space, including:

[0012] The information acquisition module is used to identify data dimensions across multiple industries based on the target cross-industry data space and obtain cross-industry data dimension relationships.

[0013] The data analysis module is used to acquire sharing requests and perform dimension migration analysis on the sharing requests based on the cross-industry data dimension relationships to acquire a set of dimension migration paths.

[0014] The data reorganization module is used to perform an adaptive reorganization operation on the data to be shared based on the dimensional migration path set, and obtain reorganized shared data;

[0015] The result generation module is used to perform cross-industry confidence assessment on the recombined shared data based on the cross-industry data dimension relationships and the dimension migration path set, and generate shared decision results based on the cross-industry confidence assessment results;

[0016] A shared execution module is used to perform data sharing on the recombined shared data through a secure channel in a trusted data space in response to the shared decision result.

[0017] This application provides one or more technical solutions, which have at least the following technical effects or advantages:

[0018] This application provides a method and system for cross-industry data sharing utilizing a trusted data space. First, it acquires cross-industry data dimensional relationships to understand the inherent connections and structures between data from different industries. Second, when faced with a sharing request, it performs dimensional migration analysis and obtains a path set, providing a clear direction for subsequent data reorganization and sharing. Third, it performs adaptive reorganization operations on the data to be shared, enabling the data to better adapt to the needs of cross-industry sharing and improving data availability and compatibility. Then, it conducts cross-industry confidence assessments on the reorganized shared data and generates sharing decision results, ensuring the security and reliability of data sharing and avoiding risks caused by data quality or security issues. Finally, it shares data through a secure channel within the trusted data space, further ensuring data security during transmission and preventing data leakage and malicious attacks.

[0019] Through the above technical solutions, this application establishes cross-industry data dimension relationships, performs dimension migration analysis and adaptive reorganization operations, and conducts cross-industry confidence assessment of reorganized and shared data. Under the premise of ensuring data security and privacy, it realizes effective sharing of cross-industry data and provides data support for the development of various industries. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 This is a flowchart illustrating the cross-industry data sharing method utilizing a trusted data space provided in an embodiment of this application.

[0022] Figure 2 This is a schematic diagram of the structure of a cross-industry data sharing system utilizing a trusted data space, provided in an embodiment of this application.

[0023] The components represented by each number in the attached diagram are explained below:

[0024] Information acquisition module 11, data analysis module 12, data reorganization module 13, result generation module 14, and shared execution module 15. Detailed Implementation

[0025] This application provides a method and system for cross-industry data sharing that utilizes a trusted data space, in order to address the technical problems of difficulties in cross-industry data sharing and waste of data resources.

[0026] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0027] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0028] In the description of this application, the term "for example" is used to mean "used as an example, illustration, or description." Any embodiment described as "for example" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use this application. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that this application can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid unnecessarily obscuring the description of this application. Therefore, this application is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed in this application.

[0029] Example 1, as Figure 1As shown, embodiments of this application provide a cross-industry data sharing method utilizing a trusted data space, including:

[0030] S10: Based on the target cross-industry data space, traverse multiple industries to identify data dimensions and obtain cross-industry data dimension relationships;

[0031] In this embodiment, within the target cross-industry data space, the data dimensions of multiple industries are first analyzed to identify the different data dimensions in each industry. Data mining algorithms are then used to filter information and determine the data dimensions included in each industry.

[0032] After identifying the data dimensions, they are organized and correlated to form an industry-semantic dimension-data item relationship. This relationship demonstrates the inherent connections between different industries, semantic dimensions, and data items, providing a foundation for subsequent cross-industry data sharing. The data is then categorized and integrated, with relevant data items assigned to corresponding semantic dimensions, and these semantic dimensions linked to specific industries.

[0033] Specifically, step S10 in the method includes:

[0034] Extract multiple data items from the target cross-industry data space and perform dimension identification to obtain the industry-semantic dimension to which each data item belongs, and establish the industry-semantic dimension-data item relationship;

[0035] Taking each data item as the analysis object, statistical analysis is carried out in combination with big data to determine the intrinsic confidence factor of each data item;

[0036] Using the industry-semantic dimension-data item relationship as an index, and combining big data to traverse each industry for statistical analysis, the set of dimension confidence factors for each industry is determined. The set of dimension confidence factors includes multiple industry-dimensional confidence factors.

[0037] The industry-semantic dimension-data item relationship, the intrinsic confidence factor, and the dimension confidence factor set are associated with the cross-industry data dimension relationship.

[0038] The semantic dimension includes at least the basic dimension, the business dimension, and the derived dimension.

[0039] In this embodiment, firstly, data is extracted from the target cross-industry data space, and dimension identification is performed to obtain the industry-semantic dimension to which each data item belongs. The semantic dimension includes the basic dimension, the business dimension, and the derived dimension. The basic dimension reflects the basic attributes and characteristics of the data, the business dimension reflects the business needs and application scenarios of the industry, and the derived dimension is obtained by calculating or combining the basic dimension and the business dimension.

[0040] Secondly, taking each data item as the analysis object, statistical analysis is carried out in conjunction with big data to determine its intrinsic confidence factor. The intrinsic confidence factor is equivalent to the confidence level of this type of data provided by information sources such as individuals or units, reflecting the reliability and accuracy of the data item itself.

[0041] Secondly, using the industry-semantic dimension-data item relationship as an index, and combining big data to traverse each industry for statistical analysis, we determine the set of dimension confidence factors for each industry. This set contains multiple industry-dimensional confidence factors, which reflect the data reliability and accuracy of different industries in various semantic dimensions.

[0042] Finally, the industry-semantic dimension-data item relationship, intrinsic confidence factor and dimension confidence factor set are correlated and output to form a complete cross-industry data dimension relationship.

[0043] The above methods were used to determine the cross-industry data dimension relationships, demonstrating the inherent connections between different industries, semantic dimensions, and data items, as well as the confidence level of each data item and industry in different dimensions, providing a basis for subsequent cross-industry data sharing.

[0044] Furthermore, the cross-industry data dimension relationship is defined by associating the industry-semantic dimension-data item relationship, the intrinsic confidence factor, and the dimension confidence factor set, including:

[0045] Based on the industry-semantic dimension-data item relationship, a cross-industry data dimension map is constructed with the target cross-industry data space as the root node, industry as the first-level node, semantic dimension as the second-level node, and data item as the third-level node.

[0046] The set of dimensional confidence factors is associated and labeled to each of the secondary nodes;

[0047] Establish the association relationship between each of the three-level nodes and the intrinsic confidence factor, and output the cross-industry data dimension map after association and annotation and the intrinsic confidence factor as the cross-industry data dimension relationship.

[0048] In this embodiment, firstly, a cross-industry data dimension map is constructed based on the industry-semantic dimension-data item relationship, using the target cross-industry data space as the root node to define the scope of the entire data sharing. Industry is used as a first-level node to display the position of different industries within the data system; semantic dimension is used as a second-level node to subdivide the data feature categories within each industry, such as basic dimensions, business dimensions, and derived dimensions; and data item is used as a third-level node to clarify the specific data content.

[0049] Secondly, the set of dimension confidence factors is associated and labeled to each secondary node. For example, in the business dimension of the financial industry, the dimension confidence factor is relatively high, indicating that the data in the business dimension of this industry is relatively reliable, and the data can be used more effectively when sharing data.

[0050] Then, the association between each third-level node and the intrinsic confidence factor is established, so that each specific data item has its own reliability indicator. For example, if the intrinsic confidence factor of a certain financial transaction data item is high, it indicates that the data item itself has good reliability and accuracy.

[0051] Finally, the cross-industry data dimension map after association and annotation is output with intrinsic confidence factors as cross-industry data dimension relationships, forming a data relationship system. This provides reference and guidance for subsequent cross-industry data sharing operations, ensuring that data can be accurately identified, filtered and utilized during the data sharing process, improving the efficiency and quality of data sharing, while ensuring the security and reliability of data.

[0052] S20: Obtain the sharing request, and perform dimension migration analysis on the sharing request based on the cross-industry data dimension relationship to obtain a set of dimension migration paths;

[0053] In this embodiment of the application, upon receiving a sharing request, a dimension migration analysis is performed on the sharing request based on the acquired cross-industry data dimension relationships. Dimension migration analysis refers to matching and transforming the data dimensions required by the requester with the data dimensions of the provider to find a suitable migration path.

[0054] First, the sharing request is parsed to clarify the data dimensions and data types required by the requester. Then, the industry and semantic dimensions related to the requested data dimensions are identified within cross-industry data dimension relationships. By comparing data items and confidence factors across different industry and semantic dimensions, possible dimension migration paths are determined.

[0055] Specifically, determining the dimensional migration path requires consideration of multiple factors. These include the semantic relevance of the data, the confidence level of the data (i.e., whether the data before and after migration is consistent in meaning and business logic), and prioritizing migration paths with higher confidence levels to ensure the quality of the migrated data.

[0056] After analysis and screening, a set of dimensional migration paths was ultimately obtained. This set contains multiple feasible dimensional migration solutions, providing various options for subsequent data reorganization operations. By analyzing dimensional migration and obtaining the path set, we can better understand the data differences between the requester and the provider, laying the foundation for effective cross-industry data sharing.

[0057] S30: Based on the set of dimensional migration paths, perform an adaptive reorganization operation on the data to be shared to obtain the reorganized shared data;

[0058] The adaptive reorganization operation includes at least one of data anonymization, data generalization, data aggregation, data synthesis, dimension selection, and dimension removal.

[0059] In this embodiment, an adaptive reorganization operation is performed on the data to be shared based on the set of dimension migration paths. The adaptive reorganization operation includes at least one of data anonymization, data generalization, data aggregation, data synthesis, dimension selection, and dimension removal.

[0060] Data anonymization involves processing sensitive information, such as masking ID card numbers and mobile phone numbers, to ensure that users' privacy information is not leaked during data sharing. Data generalization, on the other hand, involves abstracting data, such as generalizing a specific age value into an age range, which preserves the statistical characteristics of the data while reducing its sensitivity.

[0061] Furthermore, data aggregation combines multiple data items into a more representative one, such as summarizing sales data from different time periods to analyze sales trends from a macro perspective. Data synthesis integrates data from different sources to create data with new value, such as combining user consumption data and browsing data to uncover users' potential needs.

[0062] Dimension selection involves choosing the relevant dimensions from a large pool of data, removing irrelevant information, and reducing data redundancy. Dimension elimination involves directly removing dimensions that may affect data quality or security, ensuring the reliability of the shared data.

[0063] When performing adaptive reorganization operations, appropriate operation methods and combinations are dynamically selected based on the specific requirements of the dimensional migration path set.

[0064] For example, if the sharing request focuses on macro-level data analysis, data aggregation and dimension selection are more commonly used; if data security requirements are high, data anonymization and dimension removal will be prioritized. Through adaptive reorganization, the data to be shared is better adapted to the needs of cross-industry sharing, improving data availability and compatibility, and laying the foundation for subsequent data sharing.

[0065] S40: Based on the cross-industry data dimension relationships and the dimension migration path set, perform a cross-industry confidence assessment on the reorganized shared data, and generate a sharing decision result based on the cross-industry confidence assessment result;

[0066] In this embodiment, cross-industry confidence assessment is performed on the recombined shared data based on cross-industry data dimensional relationships and dimensional migration path sets. The assessment process comprehensively considers the reliability and accuracy of the data across different industries and semantic dimensions.

[0067] First, based on the confidence factor sets of each industry in the cross-industry data dimension relationships, the confidence level of the restructured and shared data under different industry dimensions is determined. For example, for the business dimension of the financial industry, if the confidence factor of this dimension is high, and the restructured and shared data involves this dimension, then the credibility of this part of the data in cross-industry sharing is relatively high.

[0068] Simultaneously, by combining the set of dimension migration paths, we analyze the changes in data confidence during the migration process. Since dimension migration may alter the original state of the data, we assess the reliability of the migrated data. For example, if a migration path with higher confidence is selected during dimension migration, the quality of the migrated data is more guaranteed.

[0069] Furthermore, based on the cross-industry confidence assessment results, a sharing decision is generated. If the assessment results show a high level of confidence in reorganizing and sharing the data, then a sharing decision is made, and the scope and method of sharing are determined.

[0070] If the assessment results indicate a low level of confidence in the data, further analysis is needed to determine the cause. This could be due to problems during data reorganization or migration, or the original data itself may lack reliability. To address these possibilities, appropriate measures should be taken, such as reorganizing the data, adjusting the dimensional migration path, or supplementing and correcting the data to improve its confidence level, before conducting another assessment and decision-making process.

[0071] By employing cross-industry confidence assessment and shared decision-making mechanisms, we ensure that only reliable and accurate data is shared during the cross-industry data sharing process, thereby improving the quality and value of data sharing and providing more effective data support for the development of various industries.

[0072] Specifically, based on the cross-industry data dimension relationships and the dimension migration path set, a cross-industry confidence assessment is performed on the reorganized shared data, including:

[0073] Based on the cross-industry data dimension relationships and the dimension migration path set, the intrinsic confidence factor, source dimension confidence factor set, and target dimension confidence factor set of the recombined shared data are extracted;

[0074] Using multiple industry-dimensional confidence factors in the source dimension confidence factor set as weights, the intrinsic confidence factors are weighted and corrected to obtain the first industry confidence set of the recombined shared data;

[0075] Based on the adaptive recombination operation record of the recombined shared data, a preset decay factor library is called to match the confidence decay factor, and decay calculation is performed on the first industry confidence set to obtain the recombined data confidence set.

[0076] Based on the target dimension confidence factor set and the intrinsic confidence factor, calculate and obtain the second industry confidence set of the recombined shared data;

[0077] The cross-industry confidence assessment of the restructured shared data is performed based on the restructured data confidence set and the second industry confidence set.

[0078] In this embodiment, firstly, the intrinsic confidence factor, source dimension confidence factor set, and target dimension confidence factor set of the recombined shared data are extracted based on the cross-industry data dimension relationships and dimension migration path set. The intrinsic confidence factor reflects the reliability and accuracy of the recombined shared data item itself, the source dimension confidence factor set reflects the data reliability of the data source industry in each semantic dimension, and the target dimension confidence factor set represents the data reliability of the data target industry in each semantic dimension.

[0079] Secondly, using multiple industry-dimensional confidence factors from the source dimension confidence factor set as weights, the intrinsic confidence factors are weighted and corrected to obtain the first industry confidence set of the reorganized shared data. By weighting and correcting the intrinsic confidence factors, the first industry confidence set better reflects the actual reliability of the data in the source industry.

[0080] For example, if the intrinsic confidence factor of a certain data item is 0.8, and the industry source of the data has an industry-dimensional confidence factor of 0.9 in the business dimension and an industry-dimensional confidence factor of 0.7 in the derived dimension.

[0081] For the business dimension, the first industry confidence level of this data item after weighted adjustment is 0.8 × 0.9 = 0.72;

[0082] For the derived dimension, the weighted and adjusted confidence level of the first industry is 0.8 × 0.7 = 0.56.

[0083] Next, based on the adaptive recombination operation records of the recombined shared data, a preset attenuation factor library is invoked to match the confidence attenuation factor, and attenuation calculations are performed on the first industry confidence set to obtain the recombined data confidence set. Since adaptive recombination operations may affect data reliability—for example, data anonymization and generalization operations may cause the loss or alteration of certain data features—attenuation calculations are needed to adjust the first industry confidence set to obtain a recombined data confidence set that better reflects the actual situation. For example, attenuation calculations can be achieved by multiplying each confidence factor in the first industry confidence set by its corresponding attenuation factor.

[0084] For example, if data desensitization is performed during adaptive recombination, the attenuation factor matched according to the preset attenuation factor library is 0.9.

[0085] The confidence level of the first industry after weighted adjustment for the above business dimensions is 0.72, and the confidence level of the recombined data after attenuation calculation is 0.72 × 0.9 = 0.648;

[0086] With a weighted confidence level of 0.56 for the first industry derived dimension, the confidence level of the recombined data after attenuation calculation is 0.56 × 0.9 = 0.504.

[0087] Next, based on the target dimension confidence factor set and the intrinsic confidence factor, a second industry confidence set for the recombined shared data is calculated. Combining the intrinsic confidence factor with consideration of the target industry's requirements for data reliability and the data's performance in the target industry dimension, the second industry confidence set is obtained, reflecting the data's reliability within the target industry.

[0088] For example, if the industry-dimensional confidence factor of the target industry in the business dimension is 0.85, the industry-dimensional confidence factor in the derived dimension is 0.75, and the intrinsic confidence factor of a certain data item is 0.8.

[0089] For the business dimension, the second industry confidence level for this data item is 0.8 × 0.85 = 0.68;

[0090] For the derived dimension, the confidence level for the second industry is 0.8 × 0.75 = 0.6.

[0091] Finally, a cross-industry confidence assessment of the restructured and shared data is conducted based on the confidence set of the restructured data and the confidence set of the second industry. Assessing the confidence of the data in the cross-industry sharing process provides a basis for generating accurate sharing decisions in the future.

[0092] Further, a cross-industry confidence assessment of the recombined shared data is performed based on the recombined data confidence set and the second industry confidence set, including:

[0093] Using data items as indexes, establish a mapping relationship between the recombined data confidence set and the second industry confidence set;

[0094] Based on the mapping relationship, the ratio of multiple recombined data confidence scores to the second industry confidence scores is calculated to obtain multiple confidence score satisfaction levels.

[0095] The combined output of multiple confidence levels constitutes the cross-industry confidence assessment result.

[0096] In this embodiment, firstly, a mapping relationship is established between the reconstructed data confidence set and the second industry confidence set, using data items as indexes. Each specific data item is mapped one-to-one with its confidence level in the target industry after reconstructing. For example, a financial transaction data item has a corresponding confidence value in the reconstructed data confidence set and also a confidence value in the target industry in the second industry confidence set.

[0097] Secondly, based on the mapping relationship, the ratios of multiple reconstructed data confidence scores to the confidence scores of the second industry are calculated to obtain multiple confidence score satisfaction levels. The confidence score satisfaction level reflects the degree of matching between the reconstructed data confidence score and the confidence score required by the target industry. If the ratio is greater than 1, it indicates that the confidence score of the reconstructed data is higher than the target industry's requirements, and the data has high reliability in cross-industry sharing; if the ratio is less than 1, it indicates that the confidence score of the reconstructed data does not meet the target industry's requirements, and further data optimization may be needed.

[0098] For example, the confidence level of the reconstructed data of a certain data item is 0.648, and the confidence level of the second industry is 0.68. The confidence satisfaction is 0.648÷0.68≈0.95, which indicates that when the data item is shared across industries, its reliability is close to the requirements of the target industry, but there is still some room for improvement.

[0099] Finally, the multiple confidence levels are merged into a cross-industry confidence assessment result. By integrating the confidence levels of all data items, a comprehensive assessment result is formed, providing a complete reference for shared decision-making.

[0100] By analyzing the results of cross-industry confidence assessments, we can gain a clear understanding of the overall reliability of reorganized shared data across industries. This allows us to make informed sharing decisions, such as determining the scope and method of sharing, and whether further data processing is required, to ensure the quality and effectiveness of cross-industry data sharing.

[0101] Specifically, based on cross-industry confidence assessment results, shared decision-making outcomes are generated, including:

[0102] Based on the relationship between the dimensional migration path set and the cross-industry data dimensions, several key data items are identified;

[0103] Traverse the cross-industry confidence assessment results and extract the confidence satisfaction level corresponding to multiple key data items;

[0104] If the confidence satisfaction level corresponding to multiple key data items is greater than or equal to 1, then the confidence satisfaction level of multiple key data items in the cross-industry confidence assessment result is set to empty, and merged with the reorganized shared data to output the shared decision result; otherwise, the shared decision result is output as empty.

[0105] In this embodiment, firstly, based on the dimensional migration path set and the cross-industry data dimension relationship, several key data items for the target industry are identified. These key data items influence the rationality of sharing decisions and the effectiveness of data sharing. For example, in a data sharing scenario between the financial and healthcare industries, financial data related to patient credit ratings and key health indicator data for patients may be identified as key data items.

[0106] Secondly, the confidence assessment results across industries are traversed to extract the confidence satisfaction levels corresponding to multiple key data items. For example, in the aforementioned financial and medical data sharing scenario, the confidence satisfaction levels corresponding to patient credit rating data and key health indicator data are extracted separately.

[0107] Specifically, if the confidence levels of multiple key data items are all greater than or equal to 1, it indicates that the reliability of the key data items in cross-industry sharing meets or exceeds the requirements of the target industry. In this case, the confidence levels of multiple key data items in the cross-industry confidence assessment result are set to null, and then merged with the recombined shared data to output the shared decision result. For example, when the confidence levels of patient credit rating data and key health indicator data are both greater than or equal to 1, the confidence levels of these two key data items are set to null, and then merged with the recombined shared data to form the final shared decision result.

[0108] Otherwise, if the confidence level of any key data item is less than 1, it indicates that the reliability of some key data items in cross-industry sharing does not meet the requirements of the target industry. In this case, the output sharing decision result is empty. This avoids unreliable data from entering the cross-industry sharing process, ensuring the quality and security of data sharing. When the confidence level is less than 1, further analysis of the reasons is needed, such as checking whether there are problems in the data reorganization process, whether the dimension migration path is appropriate, etc., and taking corresponding measures to improve, such as reorganizing the data, adjusting the dimension migration path, or supplementing and correcting the data to improve the confidence level of the key data items. Then, evaluation and decision-making are carried out again until the confidence level of all key data items is greater than or equal to 1, before effective cross-industry data sharing can be carried out.

[0109] S50: In response to the shared decision result, perform data sharing on the recombined shared data through a secure channel in the trusted data space.

[0110] In this embodiment, after receiving the sharing decision result, data sharing is performed on the recombined shared data through a secure channel in the trusted data space, based on the result. The secure channel in the trusted data space has multiple security mechanisms to effectively prevent data from being stolen, tampered with, or leaked during transmission.

[0111] First, the secure channel employs advanced encryption algorithms to encrypt the reconstructed shared data, converting it into ciphertext for transmission. During data transmission, the secure channel monitors the transmission status in real time to ensure data integrity and continuity. If any anomalies are detected, such as packet loss or excessive transmission latency, appropriate measures are taken, such as retransmitting lost packets or adjusting the transmission rate.

[0112] After data sharing is completed, the secure channel records and audits the entire process. The records include information such as the data sending and receiving time, data volume, and transmission path, facilitating subsequent traceability and retrieval. The auditing function allows for the timely detection of potential security risks and violations during data sharing, enabling appropriate measures to be taken.

[0113] By using a secure channel in a trusted data space to share reorganized and shared data, effective cross-industry data sharing can be achieved while ensuring data security, providing strong data support for the development of various industries.

[0114] Furthermore, in response to the shared decision result, performing data sharing on the recombined shared data through a secure channel in a trusted data space also includes:

[0115] Add derived data tags to the recombined shared data and associate the tags with shared lineage relationships;

[0116] Configure data reflux constraints, including:

[0117] When the recombined shared data has derived data tags, data sharing pointing to the root industry in the shared lineage is prevented;

[0118] When the generation number of the shared bloodline relationship is greater than or equal to a preset generation number constraint, data sharing of the recombined shared data is prevented.

[0119] In this embodiment, firstly, derived data tags are added to the recombined shared data, and the shared lineage of these tags is associated with them. The derived data tags indicate how the data was derived from the original data, while the shared lineage records the data's flow path and source during cross-industry sharing, facilitating the tracing and management of the data's origin and evolution. For example, in a financial and medical data sharing scenario, if data about the correlation between patients' consumption habits and health risks is derived from patients' financial transaction data, corresponding tags can be added to this derived data, and its shared lineage with the original financial transaction data can be associated with it.

[0120] Secondly, configure data backflow constraints. When recombined shared data has derived data tags, prevent data sharing pointing to the root industry in the sharing lineage, prevent data from circulating during cross-industry sharing, and avoid excessive data diffusion and potential security risks. For example, in the aforementioned financial and medical data sharing scenario, if medical-related derived data is derived from financial data, it cannot flow back to the financial industry for sharing, in order to protect the security and privacy of financial data.

[0121] Furthermore, when the relational algebra of shared kinship relationships is greater than or equal to a preset algebraic constraint, data sharing involving the reorganization of shared data is prevented. The relational algebra reflects the complexity and number of data transfers during the sharing process; the preset algebraic constraint controls the depth and scope of data sharing. Excessive data transfers or overly complex relationships may reduce data reliability and security; therefore, data sharing stops when the preset algebraic constraint is reached.

[0122] For example, an algebraic constraint of 3 is set. If a piece of data has undergone more than 3 transfers and derivations during cross-industry sharing, it will no longer be allowed to be shared further. Through the above data backflow constraint mechanism, the security and controllability of cross-industry data sharing are further guaranteed.

[0123] In summary, compared to existing technologies, this application comprehensively and meticulously considers and adjusts the reliability of recombined shared data by extracting and calculating intrinsic confidence factors, source dimension confidence factor sets, and target dimension confidence factor sets. Regarding data evaluation, cross-industry confidence assessment comprehensively considers the reliability of data in both the source and target industries, providing an accurate and objective basis for sharing decisions. In the data sharing phase, the secure channel of the trusted data space, combined with a strict data return constraint mechanism, ensures the security and controllability of data during cross-industry sharing.

[0124] In summary, the embodiments of this application have at least the following technical effects:

[0125] This application provides a method for cross-industry data sharing utilizing a trusted data space. First, it acquires cross-industry data dimensional relationships to understand the inherent connections and structures between data from different industries. Second, when faced with a sharing request, it performs dimensional migration analysis and obtains a path set, providing a clear direction for subsequent data reorganization and sharing. Third, it performs adaptive reorganization operations on the data to be shared, enabling the data to better adapt to the needs of cross-industry sharing and improving data availability and compatibility. Then, it conducts cross-industry confidence assessments on the reorganized shared data and generates sharing decision results, ensuring the security and reliability of data sharing and avoiding risks caused by data quality or security issues. Finally, it shares data through a secure channel within the trusted data space, further ensuring data security during transmission and preventing data leakage and malicious attacks. Through the above technical solutions, this application establishes cross-industry data dimensional relationships, performs dimensional migration analysis and adaptive reorganization operations, and conducts cross-industry confidence assessments on the reorganized shared data. Under the premise of ensuring data security and privacy, it achieves effective cross-industry data sharing, providing data support for the development of various industries.

[0126] Example 2, as Figure 2 As shown, based on the same inventive concept as the cross-industry data sharing method utilizing a trusted data space provided in Embodiment 1, this application also provides a cross-industry data sharing system utilizing a trusted data space, including:

[0127] Information acquisition module 11 is used to identify data dimensions across multiple industries based on the target cross-industry data space and obtain cross-industry data dimension relationships;

[0128] The data analysis module 12 is used to acquire sharing requests and perform dimension migration analysis on the sharing requests based on the cross-industry data dimension relationships to acquire a set of dimension migration paths;

[0129] Data restructuring module 13 is used to perform adaptive restructuring operation on the data to be shared based on the set of dimensional migration paths, and obtain restructured shared data;

[0130] The result generation module 14 is used to perform cross-industry confidence assessment on the recombined shared data based on the cross-industry data dimension relationship and the dimension migration path set, and generate a sharing decision result based on the cross-industry confidence assessment result.

[0131] The shared execution module 15 is used to perform data sharing on the recombined shared data through a secure channel of the trusted data space in response to the shared decision result.

[0132] In one embodiment, the information acquisition module 11 is specifically used for:

[0133] Extract multiple data items from the target cross-industry data space and perform dimension identification to obtain the industry-semantic dimension to which each data item belongs, and establish the industry-semantic dimension-data item relationship;

[0134] Taking each data item as the analysis object, statistical analysis is carried out in combination with big data to determine the intrinsic confidence factor of each data item;

[0135] Using the industry-semantic dimension-data item relationship as an index, and combining big data to traverse each industry for statistical analysis, the set of dimension confidence factors for each industry is determined. The set of dimension confidence factors includes multiple industry-dimensional confidence factors.

[0136] The industry-semantic dimension-data item relationship, the intrinsic confidence factor, and the dimension confidence factor set are associated with the cross-industry data dimension relationship.

[0137] The semantic dimension includes at least the basic dimension, the business dimension, and the derived dimension.

[0138] Further, in one embodiment of the application, the association output of the industry-semantic dimension-data item relationship, the intrinsic confidence factor, and the dimension confidence factor set constitutes the cross-industry data dimension relationship, including:

[0139] Based on the industry-semantic dimension-data item relationship, a cross-industry data dimension map is constructed with the target cross-industry data space as the root node, industry as the first-level node, semantic dimension as the second-level node, and data item as the third-level node.

[0140] The set of dimensional confidence factors is associated and labeled to each of the secondary nodes;

[0141] Establish the association relationship between each of the three-level nodes and the intrinsic confidence factor, and output the cross-industry data dimension map after association and annotation and the intrinsic confidence factor as the cross-industry data dimension relationship.

[0142] The adaptive reorganization operation includes at least one of data anonymization, data generalization, data aggregation, data synthesis, dimension selection, and dimension removal.

[0143] Further, in one embodiment of the application, a cross-industry confidence assessment is performed on the recombined shared data based on the cross-industry data dimension relationships and the dimension migration path set, including:

[0144] Based on the cross-industry data dimension relationships and the dimension migration path set, the intrinsic confidence factor, source dimension confidence factor set, and target dimension confidence factor set of the recombined shared data are extracted;

[0145] Using multiple industry-dimensional confidence factors in the source dimension confidence factor set as weights, the intrinsic confidence factors are weighted and corrected to obtain the first industry confidence set of the recombined shared data;

[0146] Based on the adaptive recombination operation record of the recombined shared data, a preset decay factor library is called to match the confidence decay factor, and decay calculation is performed on the first industry confidence set to obtain the recombined data confidence set.

[0147] Based on the target dimension confidence factor set and the intrinsic confidence factor, calculate and obtain the second industry confidence set of the recombined shared data;

[0148] The cross-industry confidence assessment of the restructured shared data is performed based on the restructured data confidence set and the second industry confidence set.

[0149] Further, in one embodiment, the cross-industry confidence assessment of the recombined shared data is performed based on the recombined data confidence set and the second industry confidence set, including:

[0150] Using data items as indexes, establish a mapping relationship between the recombined data confidence set and the second industry confidence set;

[0151] Based on the mapping relationship, the ratio of multiple recombined data confidence scores to the second industry confidence scores is calculated to obtain multiple confidence score satisfaction levels.

[0152] The combined output of multiple confidence levels constitutes the cross-industry confidence assessment result.

[0153] Furthermore, based on the cross-industry confidence assessment results, shared decision-making results are generated, including:

[0154] Based on the relationship between the dimensional migration path set and the cross-industry data dimensions, several key data items are identified;

[0155] Traverse the cross-industry confidence assessment results and extract the confidence satisfaction level corresponding to multiple key data items;

[0156] If the confidence satisfaction level corresponding to multiple key data items is greater than or equal to 1, then the confidence satisfaction level of multiple key data items in the cross-industry confidence assessment result is set to empty, and merged with the reorganized shared data to output the shared decision result; otherwise, the shared decision result is output as empty.

[0157] Furthermore, in one embodiment, in response to the sharing decision result, performing data sharing on the recombined shared data through a secure channel in a trusted data space further includes:

[0158] Add derived data tags to the recombined shared data and associate the tags with shared lineage relationships;

[0159] Configure data reflux constraints, including:

[0160] When the recombined shared data has derived data tags, data sharing pointing to the root industry in the shared lineage is prevented;

[0161] When the generation number of the shared bloodline relationship is greater than or equal to a preset generation number constraint, data sharing of the recombined shared data is prevented.

[0162] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this specification. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0163] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

[0164] This specification and accompanying drawings are merely illustrative examples of this application and are intended to cover any and all modifications, variations, combinations, or equivalents within the scope of this application. Clearly, those skilled in the art can make various alterations and modifications to this application without departing from its scope. Therefore, if such modifications and modifications fall within the scope of this application and its equivalents, this application intends to include such modifications and modifications.

Claims

1. A cross-industry data sharing method utilizing a trusted data space, characterized in that: include: Based on the target cross-industry data space, we traverse multiple industries to identify data dimensions and obtain cross-industry data dimension relationships. Obtain a sharing request, and perform dimension migration analysis on the sharing request based on the cross-industry data dimension relationship to obtain a set of dimension migration paths. Dimension migration analysis refers to matching and transforming the data dimensions required by the requester with the data dimensions of the provider in order to find a suitable migration path. Based on the set of dimensional migration paths, perform an adaptive reorganization operation on the data to be shared to obtain the reorganized shared data; Based on the set of confidence factors for each industry in the cross-industry data dimension relationship, the confidence level of the reorganized shared data under different industry dimensions is determined, and combined with the set of dimension migration paths, the confidence changes during the migration process are analyzed to achieve cross-industry confidence assessment of the reorganized shared data. Based on the cross-industry confidence assessment results, a sharing decision result is generated. In response to the shared decision result, data sharing is performed on the recombined shared data through a secure channel in the trusted data space.

2. The cross-industry data sharing method utilizing a trusted data space as described in claim 1, characterized in that, Based on the target cross-industry data space, multiple industries are traversed to identify data dimensions and obtain cross-industry data dimension relationships, including: Extract multiple data items from the target cross-industry data space and perform dimension identification to obtain the industry-semantic dimension to which each data item belongs, and establish the industry-semantic dimension-data item relationship; Taking each data item as the analysis object, statistical analysis is carried out in combination with big data to determine the intrinsic confidence factor of each data item; Using the industry-semantic dimension-data item relationship as an index, and combining big data to traverse each industry for statistical analysis, the set of dimension confidence factors for each industry is determined. The set of dimension confidence factors includes multiple industry-dimensional confidence factors. The industry-semantic dimension-data item relationship, the intrinsic confidence factor, and the dimension confidence factor set are associated with the cross-industry data dimension relationship.

3. The cross-industry data sharing method utilizing a trusted data space as described in claim 2, characterized in that, The semantic dimension includes at least the basic dimension, the business dimension, and the derived dimension.

4. The cross-industry data sharing method utilizing a trusted data space as described in claim 2, characterized in that, The associated output of the industry-semantic dimension-data item relationship, the intrinsic confidence factor, and the dimension confidence factor set constitutes the cross-industry data dimension relationship, including: Based on the industry-semantic dimension-data item relationship, a cross-industry data dimension map is constructed with the target cross-industry data space as the root node, industry as the first-level node, semantic dimension as the second-level node, and data item as the third-level node. The set of dimensional confidence factors is associated and labeled to each of the secondary nodes; Establish the association relationship between each of the three-level nodes and the intrinsic confidence factor, and output the cross-industry data dimension map after association and annotation and the intrinsic confidence factor as the cross-industry data dimension relationship.

5. The cross-industry data sharing method utilizing a trusted data space as described in claim 1, characterized in that, Based on the cross-industry data dimension relationships and the set of dimension migration paths, a cross-industry confidence assessment is performed on the reorganized shared data, including: Based on the cross-industry data dimension relationships and the dimension migration path set, the intrinsic confidence factor, source dimension confidence factor set, and target dimension confidence factor set of the recombined shared data are extracted; Using multiple industry-dimensional confidence factors in the source dimension confidence factor set as weights, the intrinsic confidence factors are weighted and corrected to obtain the first industry confidence set of the recombined shared data; Based on the adaptive recombination operation record of the recombined shared data, a preset decay factor library is called to match the confidence decay factor, and decay calculation is performed on the first industry confidence set to obtain the recombined data confidence set. Based on the target dimension confidence factor set and the intrinsic confidence factor, calculate and obtain the second industry confidence set of the recombined shared data; The cross-industry confidence assessment of the restructured shared data is performed based on the restructured data confidence set and the second industry confidence set.

6. The cross-industry data sharing method utilizing a trusted data space as described in claim 5, characterized in that, Based on the recombined data confidence set and the second industry confidence set, a cross-industry confidence assessment of the recombined shared data is performed, including: Using data items as indexes, establish a mapping relationship between the recombined data confidence set and the second industry confidence set; Based on the mapping relationship, the ratio of multiple recombined data confidence scores to the second industry confidence scores is calculated to obtain multiple confidence score satisfaction levels. The combined output of multiple confidence levels constitutes the cross-industry confidence assessment result.

7. The cross-industry data sharing method utilizing a trusted data space as described in claim 6, characterized in that, Based on the cross-industry confidence assessment results, shared decision-making results are generated, including: Based on the relationship between the dimensional migration path set and the cross-industry data dimensions, several key data items are identified; Traverse the cross-industry confidence assessment results and extract the confidence satisfaction level corresponding to multiple key data items; If the confidence satisfaction level corresponding to multiple key data items is greater than or equal to 1, then the confidence satisfaction level of multiple key data items in the cross-industry confidence assessment result is set to empty, and merged with the reorganized shared data to output the shared decision result; otherwise, the shared decision result is output as empty.

8. The cross-industry data sharing method utilizing a trusted data space as described in claim 1, characterized in that, The adaptive reorganization operation includes at least one of the following: data anonymization, data generalization, data aggregation, data synthesis, dimension selection, and dimension removal.

9. The cross-industry data sharing method utilizing a trusted data space as described in claim 1, characterized in that, In response to the shared decision result, data sharing is performed on the recombined shared data through a secure channel in a trusted data space, including: Add derived data tags to the recombined shared data and associate the tags with shared lineage relationships; Configure data reflux constraints, including: When the recombined shared data has derived data tags, data sharing pointing to the root industry in the shared lineage is prevented; When the relational algebra of the shared bloodline is greater than or equal to a preset algebraic constraint, the sharing of the recombined shared data is prevented, wherein the relational algebra is used to reflect the complexity and number of transfers of the data during the sharing process.

10. A cross-industry data sharing system utilizing a trusted data space, characterized in that: A method for performing cross-industry data sharing using a trusted data space as described in any one of claims 1-9, comprising: The information acquisition module is used to identify data dimensions across multiple industries based on the target cross-industry data space and obtain cross-industry data dimension relationships. The data analysis module is used to obtain sharing requests and perform dimension migration analysis on the sharing requests based on the cross-industry data dimension relationships to obtain a set of dimension migration paths. Dimension migration analysis refers to matching and transforming the data dimensions required by the requester with the data dimensions of the provider in order to find a suitable migration path. The data reorganization module is used to perform an adaptive reorganization operation on the data to be shared based on the dimensional migration path set, and obtain reorganized shared data; The result generation module is used to determine the confidence level of the recombined shared data under different industry dimensions based on the confidence factor set of each industry in the cross-industry data dimension relationship, and to analyze the confidence changes during the migration process in combination with the dimension migration path set, so as to realize the cross-industry confidence assessment of the recombined shared data, and generate sharing decision results based on the cross-industry confidence assessment results. A shared execution module is used to perform data sharing on the recombined shared data through a secure channel in a trusted data space in response to the shared decision result.

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