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Differential privacy promises to enable general data analytics whileprotecting individual privacy, but existing differential privacy mechanisms donot support the wide variety of features and databases used in real-worldSQL-based analytics systems.  This paper presents the first practical approach for differential privacy ofSQL queries. Using 8.1 million real-world queries, we conduct an empiricalstudy to determine the requirements for practical differential privacy, anddiscuss limitations of previous approaches in light of these requirements. Tomeet these requirements we propose elastic sensitivity, a novel method forapproximating the local sensitivity of queries with general equijoins. We provethat elastic sensitivity is an upper bound on local sensitivity and cantherefore be used to enforce differential privacy using any localsensitivity-based mechanism.  We build FLEX, a practical end-to-end system to enforce differential privacyfor SQL queries using elastic sensitivity. We demonstrate that FLEX iscompatible with any existing database, can enforce differential privacy forreal-world SQL queries, and incurs negligible (0.03%) performance overhead.

Towards Practical Differential Privacy for SQL Queries