Privacy-Preserving Data Space Analytics for Secure Data Sharing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Stakeholders are reluctant to share or exchange data due to concerns about exposing valuable trade secrets or private information, hindering the development of a data economy.
Innovation Solution
A privacy-preserving data space platform that enables automated data sharing and analytics using a semantic knowledge graph, allowing stakeholders to collaborate while keeping data secure, by semantically annotating data and using semantic analytics templates and value exchange templates to facilitate data utilization and secure data sharing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If stakeholders share or exchange data to improve application services, then service quality is improved, but data security and privacy are compromised
Solution Approach 1:
The patent introduces a data space platform as an intermediary that enables data sharing between stakeholders without direct exposure of raw data. The platform uses semantic knowledge graphs and privacy-preserving technologies to act as a mediator, allowing analytics services to be performed on data while keeping the actual data secure and accessible only to authorized stakeholders.
Solution Approach 2:
The patent creates semantic copies of data in the form of structured knowledge graphs that capture the essential information needed for analytics without exposing the original sensitive data. These semantic representations allow stakeholders to perform analyses on copied information while the original data remains protected in secure environments.
2Object-affected harmful factors
If stakeholders keep data secure to protect trade secrets, then data security is maintained, but data sharing and collaboration are hindered
Solution Approach 1:
The patent segments data into different levels of access and representation. Raw sensitive data is kept secure and segmented from analytics processing, while semantic knowledge graphs provide a segmented, structured representation that enables collaboration. The system divides data access into controlled portions based on stakeholder roles and requirements.
Solution Approach 2:
The patent transforms data from its original form into a different dimensional representation through semantic knowledge graphs. This dimensional transformation allows data to be used for analytics in a structured, queryable format while maintaining security boundaries. The semantic layer adds a new dimension of abstraction that enables sharing without exposure.
3Measurement precision
If comprehensive data is shared to improve analytics accuracy, then analysis quality is improved, but computational resource consumption increases
Solution Approach 1:
The patent performs preliminary structuring and semantic annotation of data before analytics operations. By organizing data into knowledge graphs with predefined schemas and relationships in advance, the system reduces the computational burden during actual analytics execution. This preliminary organization enables more efficient querying and analysis without requiring intensive processing of raw data.
Data Source
AI summary
A computer-implemented method for performing automated sharing of data and analytics across a data space platform includes receiving a request for a data analytics service from a first data stakeholder, determining first semantic data for the service based on the first data stakeholder and the request, and extracting second semantic data from a second data stakeholder based on comparing relevance to the first semantic data. The first semantic data comprises raw data and semantic annotations, stored as a knowledge graph. An analytics insight based on the first and second semantic data is provided to the first data stakeholder without revealing the second semantic data. The method can be applied to machine learning and regression problems (continuous values) including, but not limited to, providing improvements to various technical fields such as medical diagnosis and treatment, operation system design and optimization, material design and optimization, telecommunication network design, decision making and optimization.