Query Auditing Using Differentials for Privacy and Coverage
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional single tuple auditing approaches have limited real-world utility due to their restricted range of query classes and significant privacy limitations, making them inadequate for complex queries and potentially insecure.
Innovation Solution
A query auditing methodology using query differentials is introduced, where queries are characterized as 'safe' or 'unsafe' by comparing their results with those of their differentials, enabling efficient auditing of arbitrary and complex queries while providing strong privacy assurances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If instance independent approach is used for auditing, then privacy guarantees are strong, but the range of query classes that can be audited efficiently is limited
Solution Approach 1:
The patent segments the auditing approach into two distinct methods: instance independent auditing for strong privacy guarantees and instance dependent auditing for broader query class support. The system selectively applies each approach based on the specific auditing scenario and query type, allowing both privacy protection and versatile query auditing to coexist without forcing a single approach to handle all cases
Solution Approach 2:
The patent creates a universal auditing framework that can handle both instance independent and instance dependent auditing scenarios. The system is designed to automatically select the appropriate auditing method based on the query characteristics and privacy requirements, making the auditing system adaptable to diverse query classes while maintaining strong privacy guarantees when needed
2Adaptability or versatility
If instance dependent approach is used for auditing, then query auditing coverage is broader, but privacy limitations are severe
Solution Approach 1:
The patent implements a dynamic auditing system that can switch between instance independent and instance dependent approaches based on the specific query being audited. The system dynamically selects the appropriate auditing method by analyzing query characteristics, allowing it to provide broad query coverage when privacy risks are low and switch to privacy-preserving mode when needed, making the privacy protection adaptive rather than static
Solution Approach 2:
The patent changes the auditing parameter (instance independence) based on the query characteristics and privacy requirements. The system adjusts the level of instance dependence in auditing by analyzing query patterns and selecting the appropriate auditing depth, allowing it to provide comprehensive query coverage for safe queries while maintaining strong privacy guarantees for sensitive operations
3Productivity
If auditing system considers restricted class of queries, then auditing efficiency is improved, but the system is fundamentally incomplete
Solution Approach 1:
The patent performs preliminary analysis of queries to categorize them into different classes before applying auditing. By pre-classifying queries based on their characteristics and potential privacy risks, the system can apply efficient instance independent auditing to simple queries while reserving instance dependent auditing for complex queries that require more thorough analysis, thus maintaining both efficiency and completeness
Data Source
AI summary
Methods and systems for auditing queries using query differentials are disclosed. A method includes identifying a set of queries, determining if results of each query of the set of queries are different from results of respective differentials of each query of the set of queries, and based on the determining, making a characterization of each query of the set of queries as one of safe and unsafe. Access is provided to the characterization of each query of the set of queries.