# Towards Practical Differential Privacy for SQL Queries

arXiv: https://arxiv.org/abs/1706.09479 · HTML: https://mlsanity.com/paper/1706.09479/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jun 2017.

## Built on by (most cited first, 19 of 19)

- [Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning](https://mlsanity.com/paper/2206.02465/) (2022)
- [Decision Making with Differential Privacy under a Fairness Lens](https://mlsanity.com/paper/2105.07513/) (2021)
- [Open Source Vizier: Distributed Infrastructure and API for Reliable and Flexible Blackbox Optimization](https://mlsanity.com/paper/2207.13676/) (2022)
- [FedImpro: Measuring and Improving Client Update in Federated Learning](https://mlsanity.com/paper/2402.07011/) (2024)
- [FuseFL: One-Shot Federated Learning through the Lens of Causality with Progressive Model Fusion](https://mlsanity.com/paper/2410.20380/) (2024)
- [Privacy-preserving data release leveraging optimal transport and particle gradient descent](https://mlsanity.com/paper/2401.17823/) (2024)
- [A Central Limit Theorem for Differentially Private Query Answering](https://mlsanity.com/paper/2103.08721/) (2021)
- [QuerySnout: Automating the Discovery of Attribute Inference Attacks against Query-Based Systems](https://mlsanity.com/paper/2211.05249/) (2022)
- [Photonic Differential Privacy with Direct Feedback Alignment](https://mlsanity.com/paper/2106.03645/) (2021)
- [DP-XGBoost: Private Machine Learning at Scale](https://mlsanity.com/paper/2110.12770/) (2021)
- [Answering Private Linear Queries Adaptively using the Common Mechanism](https://mlsanity.com/paper/2212.00135/) (2022)
- [HRNet: Differentially Private Hierarchical and Multi-Resolution Network for Human Mobility Data Synthesization](https://mlsanity.com/paper/2405.08043/) (2024)
- [Differentially Private Synthetic Data Generation for Relational Databases](https://mlsanity.com/paper/2405.18670/) (2024)
- [Learning Differentially Private Mechanisms](https://mlsanity.com/paper/2101.00961/) (2021)
- [Towards General-purpose Infrastructure for Protecting Scientific Data Under Study](https://mlsanity.com/paper/2110.01315/) (2021)
- [Differential Privacy in Machine Learning: A Survey from Symbolic AI to LLMs](https://mlsanity.com/paper/2506.11687/) (2025)
- [Private Queries with Sigma-Counting](https://mlsanity.com/paper/2509.07018/) (2025)
- [HoGS: Homophily-Oriented Graph Synthesis for Local Differentially Private GNN Training](https://mlsanity.com/paper/2602.08762/) (2026)
- [Policy-Conditioned Constrained Decoding for Column-Level Access Control in Text-to-SQL](https://mlsanity.com/paper/2607.12341/) (2026)

## Abstract (arXiv)

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.