Towards Practical Differential Privacy for SQL Queries (1706.09479v5)

Abstract: Differential privacy promises to enable general data analytics while protecting individual privacy, but existing differential privacy mechanisms do not support the wide variety of features and databases used in real-world SQL-based analytics systems. This paper presents the first practical approach for differential privacy of SQL queries. Using 8.1 million real-world queries, we conduct an empirical study to determine the requirements for practical differential privacy, and discuss limitations of previous approaches in light of these requirements. To meet these requirements we propose elastic sensitivity, a novel method for approximating the local sensitivity of queries with general equijoins. We prove that elastic sensitivity is an upper bound on local sensitivity and can therefore be used to enforce differential privacy using any local sensitivity-based mechanism. We build FLEX, a practical end-to-end system to enforce differential privacy for SQL queries using elastic sensitivity. We demonstrate that FLEX is compatible with any existing database, can enforce differential privacy for real-world SQL queries, and incurs negligible (0.03%) performance overhead.

• The paper presents elastic sensitivity, a novel method that bounds local sensitivity in SQL queries with equijoins.
• The study introduces FLEX, an end-to-end system that integrates differential privacy into existing SQL engines with negligible overhead (≈0.03%).
• Experimental validation on millions of real-world queries demonstrates high utility, effectively bridging theory and practice in DP for databases.

Analyzing Differential Privacy in SQL Queries: Elastic Sensitivity and Practical Implementation

This paper presents a significant contribution towards implementing differential privacy (DP) in SQL queries, addressing prevalent limitations in existing approaches by introducing a novel method named elastic sensitivity. The research by Johnson, Near, and Song attempts to reconcile the practical demands of real-world database systems with the theoretical guarantees of differential privacy.

The paper outlines the challenge of implementing DP mechanisms for SQL, due to the complexity and variety of queries involving multiple tables through joins—a frequent occurrence in practical analytics environments. Existing approaches, such as PINQ, wPINQ, and others, either fail to support certain join constructions or necessitate custom database engines, making them incompatible with heterogeneous database environments.

Key Contributions

1. Empirical Study: The authors conducted an extensive empirical analysis utilizing 8.1 million real-world SQL queries. This led to the identification of core requirements for practical DP systems: compatibility with existing database engines and robust support for equijoin operations, which account for a majority of SQL joins in practice.
2. Elastic Sensitivity: The core innovation of the paper is the development of elastic sensitivity, an upper bound approximation for the local sensitivity of queries containing general equijoins. Unlike global sensitivity measures, which could be infinite for queries involving joins, elastic sensitivity provides a bounded, practical method to secure DP in SQL analytics.
3. System Design and Implementation: Built as an end-to-end system compatible with any existing SQL-based database, the tool analyzes a query’s structure to compute its elastic sensitivity and enforces privacy by perturbing the output through a mechanism based on smooth sensitivity. The system, named FLEX, introduces negligible computational overhead (approximately 0.03%), ensuring its feasibility in large-scale database applications.
4. Experimental Validation: The paper offers a robust experimental evaluation, presenting evidence of high utility for a large range of real-world queries. The results, obtained from a dataset of 9862 queries, showed FLEX providing acceptable utility particularly for queries accessing larger datasets, consistent with theoretical expectations of differential privacy.

Implications and Future Directions

The implication of this work is multifaceted. Practically, it enables organizations to apply differential privacy to complex SQL queries without altering their existing database infrastructure. Theoretically, it provides a new method to approximate local sensitivity for a broad class of SQL queries, extending the application scope of DP mechanisms.

Future research could focus on extending elastic sensitivity to support more complex join conditions beyond equijoins and explore optimizations for special query types where elastic sensitivity might produce loose bounds. Additionally, automated management of privacy budgets for dynamic data settings remains a critical area for future exploration, ensuring consistent privacy protection under evolving data conditions.

This paper sets a significant precedent for practical differential privacy solutions in SQL, demonstrating an effective blend of rigorous theoretical modeling with real-world applications to meet existing and future privacy needs in data analytics.