Differentially Private SQL with Bounded User Contribution

TL;DR

This paper introduces a scalable, end-to-end SQL-based system for differential privacy that handles users with multiple data entries, addressing a key limitation of prior systems and validated on industry benchmarks.

Contribution

It presents a novel, generic method for differentially private aggregations with arbitrary user contributions, implemented as an SQL operator and validated through practical testing.

Findings

Effective utility on industry benchmarks

Correctness verified with stochastic testing

Open-source implementation available

Abstract

Differential privacy (DP) provides formal guarantees that the output of a database query does not reveal too much information about any individual present in the database. While many differentially private algorithms have been proposed in the scientific literature, there are only a few end-to-end implementations of differentially private query engines. Crucially, existing systems assume that each individual is associated with at most one database record, which is unrealistic in practice. We propose a generic and scalable method to perform differentially private aggregations on databases, even when individuals can each be associated with arbitrarily many rows. We express this method as an operator in relational algebra, and implement it in an SQL engine. To validate this system, we test the utility of typical queries on industry benchmarks, and verify its correctness with a stochastic test framework we developed. We highlight the promises and pitfalls learned when deploying such a system in practice, and we publish its core components as open-source software.