Relational Data Masking for Selective Privacy Protection
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Solution Overview
Problem
Current data masking methods mask unnecessary data, affecting flexibility and accuracy, as they fail to distinguish between data that needs to be masked and data that does not.
Innovation Solution
A data masking method that displays relational data groups, allowing users to select target data for masking, and performs masking based on user-defined criteria such as masking targets and degrees, enabling flexible and accurate masking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If automatic privacy data discovery technology is used to mask all detected privacy data, then data protection coverage is improved, but masking accuracy deteriorates because unnecessary data is also masked
Solution Approach 1:
The patent segments the masking process into two distinct phases: automatic discovery of privacy data and user-selective masking. The system first automatically identifies all potential privacy data, then presents this data to users for selective confirmation. This segmentation allows the system to maintain comprehensive discovery coverage while enabling users to control the final masking scope, thus resolving the contradiction between coverage and accuracy.
Solution Approach 2:
The patent introduces a user interface as an intermediary between the automatic discovery system and the final masking execution. The interface displays discovered privacy data and allows users to select which items should be masked. This intermediary layer enables users to override automatic detection results, ensuring that only truly necessary data is masked while maintaining the benefits of automatic discovery for comprehensive identification.
2Reliability
If all discovered privacy data is masked automatically, then data protection completeness is improved, but flexibility deteriorates because users cannot exclude non-sensitive data
Solution Approach 1:
The patent implements a dynamic masking approach where the masking scope is not fixed but can be adjusted by users. The system initially identifies all privacy data dynamically, then allows users to dynamically adjust which items should be masked based on their specific needs. This dynamic adjustment capability provides both comprehensive protection (when users select all items) and flexible customization (when users select only specific items), resolving the contradiction between completeness and flexibility.
Solution Approach 2:
The patent performs preliminary identification of all potential privacy data before the final masking decision. By pre-discovering and presenting all candidate privacy data to users, the system ensures that no sensitive data is accidentally omitted while giving users the opportunity to exclude non-sensitive items. This preliminary action maintains protection completeness while enabling subsequent flexible user control.
3Measurement precision
If user selection interface is provided for each data item, then masking accuracy is improved, but operation complexity increases
Solution Approach 1:
The patent merges multiple individual data items into a unified display interface that presents all discovered privacy data together. Instead of requiring separate operations for each data item, the system consolidates them in a single view where users can review and select multiple items simultaneously. This merging approach maintains high masking accuracy through user selection while significantly reducing operation complexity by eliminating repetitive individual operations.
Data Source
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AI summary
This application discloses a data masking method and a device, and belongs to the field of data processing. The 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. In this application, flexible masking of data in the data source can be implemented. This application is used for data masking.