Real-time Database Anonymization via Sequence Column Metadata
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Solution Overview
Problem
Current database solutions require comprehensive comparisons of table data to detect changes, which is inefficient and may not support real-time analytics or anonymization, especially when dealing with sensitive data.
Innovation Solution
Implementing a sequence column in tables to track changes by incrementing unique integer values for new entries, allowing for the detection of changes without analyzing specific data entries, and applying real-time anonymization based on these changes to ensure privacy compliance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comprehensive comparison of table data is performed to detect changes, then change detection accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The patent extracts only the essential change detection information (row count and sequence column maximum value) from the complete table data, eliminating the need to compare all data entries. This extraction approach maintains change detection accuracy while dramatically reducing processing time and computational resources required.
Solution Approach 2:
Instead of performing a complete comparison of all table data, the patent applies partial action by only checking the row count and maximum sequence value. This partial inspection is sufficient to detect changes without the overhead of comprehensive data comparison.
2Reliability
If complete table data is stored for change detection, then change detection capability is improved, but storage requirements increase
Solution Approach 1:
The patent extracts only the necessary metadata (row count and sequence column maximum value) from the complete table data for storage. This extraction maintains full change detection capability while minimizing storage requirements to just these two scalar values per table.
Solution Approach 2:
Instead of storing complete table data copies for change detection, the patent stores simplified metadata copies (count and max sequence value) that are sufficient for change detection purposes, significantly reducing storage requirements.
3Object-affected harmful factors
If static anonymization is performed during preprocessing, then privacy protection is improved, but real-time analytics capability deteriorates
Solution Approach 1:
The patent transitions from static anonymization to dynamic, real-time anonymization by applying anonymization operations at query execution time based on sequence column values. This dynamic approach maintains privacy protection while enabling real-time analytics capability.
Solution Approach 2:
The patent performs preliminary setup of the sequence column and anonymization rules, but delays the actual anonymization execution until query time. This preliminary preparation enables fast real-time anonymization without compromising privacy protection.
Data Source
AI summary
Techniques are described for real time anonymization. In one example, a first a first query associated with a table is received, the table associated with a first anonymization operation satisfying satisfies a privacy requirement. The first anonymization operation is applied at runtime to generate a first anonymized data set against which the first query is executed. At T2 after T1, a subsequent query is received. A determination is made as to whether a change to the table has occurred since T1. If so, a determination is made as to whether the first anonymization operation satisfies the privacy requirement as applied to the current table. If the operation does not satisfy the privacy requirement when applied to the current table, a second anonymization operation satisfying the defined privacy requirement is determined and applied at runtime to generate a second anonymized data set against which the subsequent query can be executed.