Dynamic Anonymization Rules for Relational Database Privacy
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Solution Overview
Problem
Conventional relational databases struggle to maintain k-anonymity in query responses due to changes in database content, such as new records being added or existing records being deleted, which can compromise data anonymity.
Innovation Solution
A system that dynamically applies anonymization rules to query results by determining the frequency of attributes in the database and generating a rule set to suppress or generalize attributes, ensuring k-anonymity is maintained across changing database conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional relational databases store and query personal data without dynamic anonymization, then data utility and query responsiveness are improved, but k-anonymity cannot be maintained when database content changes
Solution Approach 1:
The system dynamically adjusts anonymization rules based on current database content. When records are added or deleted, the system recalculates cohort sizes and modifies suppression rules accordingly, transitioning from static to dynamic anonymization to maintain k-anonymity under changing conditions
Solution Approach 2:
The system continuously monitors database content changes and query patterns, using this feedback to adjust anonymization rules. The feedback loop ensures that when cohort sizes change due to database modifications, the system responds by modifying suppression rules to maintain the required k-anonymity level
2Reliability
If anonymization rules are applied to maintain k-anonymity, then privacy protection is improved, but data utility and query accuracy deteriorate
Solution Approach 1:
The system applies different anonymization rules to different cohorts based on their specific characteristics and risk levels. High-risk cohorts with small sizes receive stronger anonymization, while large cohorts maintain more data fidelity, optimizing the balance between privacy and utility locally for each group
Solution Approach 2:
The system dynamically changes anonymization parameters such as suppression thresholds and cohort size requirements based on database content. By adjusting these parameters, the system adapts the level of anonymization to current conditions, preserving data utility when possible while maintaining privacy when necessary
3Reliability
If dynamic anonymization rules are applied to maintain k-anonymity under changing database conditions, then privacy protection is improved, but system complexity increases
Solution Approach 1:
The system segments the database into distinct cohorts based on quasi-identifier values and applies anonymization rules at the cohort level rather than globally. This segmentation allows independent management of different data groups, simplifying the overall system architecture while maintaining k-anonymity across the entire database
Solution Approach 2:
The system automatically detects database content changes and triggers recalculation of anonymization rules without external intervention. This self-service mechanism reduces operational complexity by eliminating manual monitoring and rule adjustment, allowing the system to maintain k-anonymity autonomously
Data Source
AI summary
Systems, methods, and computer program products are configured to apply k-anonymity to an answer to a query sent to a relational database. A query to the relational database is obtained, the relational database containing a plurality of records. A frequency of occurrence of the attributes in the relational database is determined, an anonymization rule set is created based on the frequency of occurrence of the attributes, the anonymization rule set defining which attributes are to be suppressed in the answer to the query, the anonymization rule set is used to generate the answer to the query, wherein the answer to the query has k-anonymity, and a display or other device is controlled based on the answer to the query.