Database Differential Privacy Policies for Budget-Aware Query Optimization
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Solution Overview
Problem
Existing techniques for managing differential privacy in database systems are resource-intensive and often compromise analytical utility while failing to protect individual privacy, and there is a need for a system that can efficiently manage privacy budgets to ensure strong privacy guarantees and high utility in data sharing and collaboration.
Innovation Solution
A differential privacy policy-based system that implements privacy budgets, allows flexible access control, and supports fine-grained privacy management across tables and views, enabling administrators to configure policies and ensure privacy while maximizing utility through a differentially private query optimizer.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing techniques for managing differential privacy are used, then privacy protection is provided, but resource consumption increases and analytical utility is compromised
Solution Approach 1:
The system dynamically adjusts privacy parameters (epsilon values) and resource allocation based on query characteristics and sensitivity analysis. By changing parameters adaptively rather than using fixed conservative values, the system achieves strong privacy guarantees while optimizing resource utilization and maintaining analytical utility.
Solution Approach 2:
The differential privacy management system implements dynamic budget allocation and policy adjustment mechanisms. Privacy budgets are allocated dynamically based on query patterns, data sensitivity, and resource availability, allowing the system to adapt to changing conditions and avoid the inefficiency of static, overly conservative approaches.
2Reliability
If existing techniques for managing differential privacy are used, then privacy protection is provided, but analytical utility is compromised
Solution Approach 1:
The system applies differential privacy with varying degrees of protection to different data elements, queries, or users based on their sensitivity and importance. By implementing local quality control where high-value analytical queries receive optimized privacy handling and sensitive queries receive stronger protection, the system preserves analytical utility while maintaining privacy guarantees.
Solution Approach 2:
The system dynamically adjusts privacy parameters such as epsilon values based on query characteristics, data sensitivity assessments, and utility requirements. This adaptive parameter adjustment allows the system to minimize information loss for high-utility queries while maintaining strong privacy protection where needed, resolving the contradiction between privacy and analytical utility.
3Adaptability or versatility
If fine-grained privacy management is implemented, then privacy control is improved, but system complexity increases
Solution Approach 1:
The system segments privacy management into modular components including policy definitions, budget allocation mechanisms, query evaluation modules, and sensitivity analysis functions. This segmentation allows fine-grained control over different aspects of privacy management while keeping each component manageable and reusable, reducing overall system complexity despite the fine-grained nature of control.
Solution Approach 2:
The differential privacy management system implements universal mechanisms that handle multiple privacy control scenarios through common infrastructure. Policies, budgets, and evaluation functions serve multiple purposes across different queries and data sets, providing fine-grained control flexibility without proportionally increasing system complexity through repetition.
Data Source
AI summary
Various embodiments provide for managing differential privacy on a database system using one or more differential privacy policies and one or more differential privacy budgets associated with the one or more differential privacy policies.