Database Query Data Masking for Secure Sharing
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Solution Overview
Problem
Existing techniques for sharing and protecting sensitive data in database management systems fail to effectively replicate real-world test cases and pose risks of data corruption during masking, while also not providing minimal information sharing solutions.
Innovation Solution
A method that intercepts database queries, unmasking sensitive data with equivalent real values, and re-masks results to preserve data characteristics, allowing for dynamic masking without encryption, enabling secure sharing while maintaining referential integrity and facilitating diagnostic processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If production data is shared with constituents for business purposes, then data utility and accessibility are improved, but sensitive information exposure and security risks increase
Solution Approach 1:
The patent segments data into sensitive and non-sensitive components, applying different masking treatments to each. The system identifies and masks only the sensitive portions of data while leaving non-sensitive data accessible, thereby enabling data sharing without exposing sensitive information.
Solution Approach 2:
The patent applies local quality by implementing context-dependent masking where the degree and type of masking varies based on the specific data element, user role, and access context. Different masking strategies are applied to different columns or fields based on their sensitivity levels and the intended use case.
2Reliability
If data masking is applied to protect sensitive information, then security is improved, but data characteristics and referential integrity may be corrupted
Solution Approach 1:
The patent creates masked copies of production data for testing and development purposes. These copies maintain the structural characteristics, relationships, and referential integrity of the original data while having sensitive values replaced with masked equivalents, thus preserving data utility without compromising security.
Solution Approach 2:
The patent changes the parameter values of sensitive data elements while maintaining their data types, formats, and relationships. This allows masked data to retain its structural characteristics and referential integrity while the actual sensitive values are transformed into protected representations.
3Productivity
If random test data is generated based on masking algorithms, then data generation speed is improved, but authenticity and real-world scenario representation are lost
Solution Approach 1:
The patent performs preliminary actions by pre-processing production data to create masked versions before they are needed for testing. This involves identifying sensitive elements, applying masking rules, and creating ready-to-use masked datasets that retain real-world characteristics without requiring real-time data generation or transformation during testing operations.
Data Source
AI summary
Aspects include data masking in database operations including intercepting a database query and identifying masked data in the query. The masked data in the query is unmasked by substituting the masked data with equivalent real values. The unmasked query is sent to the database engine, and a result provided by the database engine in response to the unmasked query is intercepted. The values in the result that correspond to sensitive information are identified. The result is masked by substituting the values in the result corresponding to sensitive information with masked equivalents. The masked result is returned to a user.