Relational Data Masking for Accurate Privacy Selection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current data masking methods inadvertently mask non-privacy data, compromising masking flexibility and accuracy.
Innovation Solution
A data masking method that displays relational data groups, allowing users to select target data for masking, with options for masking targets and degrees, and supports user input for masking processing triggers and confidence assessment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If privacy data discovery technology is used to automatically mask discovered data, then data protection coverage is improved, but masking accuracy deteriorates because non-privacy data is also masked
Solution Approach 1:
The patent introduces an intermediary review mechanism where a user interface acts as a mediator between the automatic discovery system and the final masking execution. The system first discovers potential privacy data, then presents it to the user for verification and selection before applying masks, thus resolving the contradiction between automated coverage and accuracy.
Solution Approach 2:
The system performs preliminary discovery and presentation of candidate privacy data to the user before the actual masking action is executed. This preliminary step allows users to review and confirm which discovered data should actually be masked, preventing premature or incorrect masking of non-privacy data.
2Productivity
If automatic masking is applied to all discovered privacy data, then processing efficiency is improved, but masking flexibility deteriorates
Solution Approach 1:
The system transitions from a static automatic masking approach to a dynamic hybrid approach where the masking process can adapt based on user input. The system maintains automated discovery efficiency while incorporating dynamic user control for confirmation and selection, allowing flexibility in whether to mask individual items or groups of items.
Solution Approach 2:
The user interface serves multiple functions: it can display individual items for review, allow batch selection of multiple items, enable group-based masking, and provide options for different masking approaches. This multi-functionality resolves the contradiction by making the system both efficient (automated discovery) and flexible (multiple user control options).
Data Source
AI summary
A data masking method includes: displaying one or more groups of relational data, where the relational data includes a data subject, privacy data, and a relationship that the data subject and the privacy data meet; obtaining selection of a user for target relational data in the one or more groups of relational data; and performing masking processing on the target relational data in a data source.