Privacy-Aware Query Management System for Differential Privacy
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Solution Overview
Problem
Existing relational database management systems face challenges in protecting individual privacy when joining datasets, as even anonymized data can be re-identified through linkage attacks, leading to potential exposure of sensitive information, and conventional query planners optimize for computing resources rather than privacy parameters.
Innovation Solution
A privacy-aware query management system that employs differential privacy and a specialized query planner to optimize query plans based on privacy parameters, applying noise to query results to ensure indistinguishability and limit the number of operations, thereby protecting individual privacy by minimizing the impact on privacy budgets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional query planners optimize for computing resources, then processing efficiency is improved, but privacy protection deteriorates
Solution Approach 1:
The patent transforms the query optimization parameters from computing resource metrics (CPU, memory, I/O) to privacy-centric parameters including differential privacy budget consumption, noise magnitude, and privacy guarantee levels. The query planner evaluates multiple execution plans based on their impact on privacy parameters rather than computational efficiency alone.
Solution Approach 2:
The patent introduces a privacy-aware query planner as an intermediary layer between the standard query optimizer and the database execution engine. This intermediary translates conventional SQL queries into privacy-preserving query plans that incorporate differential privacy mechanisms, noise addition, and controlled data access patterns without requiring changes to the underlying database system.
2Reliability
If noise is applied to query results to protect privacy, then privacy guarantees are improved, but data accuracy deteriorates
Solution Approach 1:
The patent applies noise selectively rather than uniformly across all query results. The amount of noise added is calibrated based on the specific query's privacy requirements, the sensitivity of the requested data, and the remaining privacy budget. This partial application of noise preserves accuracy for less sensitive queries while providing stronger protection for sensitive data access.
Solution Approach 2:
The patent implements dynamic noise adjustment where the magnitude and type of noise added to query results adapt based on runtime conditions including the current privacy budget status, query complexity, data sensitivity classification, and historical access patterns. This dynamic approach optimizes the balance between privacy protection and data utility.
Data Source
AI summary
A privacy-aware query management system receives a query to aggregate data from a number of datasets, wherein at least one of the datasets contains private information for one or more people. The privacy-aware query management system retrieves data from the datasets. Each of the plurality of datasets has associated privacy parameters that determine a level of noise to be applied to query results obtained from that dataset, and the levels of noise are used to determine an order of operations for the query to retrieve the data from the datasets. The privacy-aware query management system applies the levels of noise to each query result obtained from each of the datasets in accordance with the privacy parameters associated with that dataset, computes aggregated data from the query results with the levels of noise applied, and responds to the query with the aggregated data.