DPSQL+: A Differentially Private SQL Library with a Minimum Frequency Rule

TL;DR

DPSQL+ is a modular, privacy-preserving SQL library that enforces differential privacy and a minimum frequency rule, enabling practical, accurate data analysis while maintaining strong privacy guarantees.

Contribution

It introduces a novel architecture combining query validation, privacy accounting, and database interfacing to enforce DP and minimum frequency rules in SQL queries.

Findings

Achieves practical accuracy on TPC-H benchmarks for various SQL operations.

Allows more queries under a fixed privacy budget compared to prior solutions.

Successfully enforces minimum frequency rule alongside differential privacy.

Abstract

SQL is the de facto interface for exploratory data analysis; however, releasing exact query results can expose sensitive information through membership or attribute inference attacks. Differential privacy (DP) provides rigorous privacy guarantees, but in practice, DP alone may not satisfy governance requirements such as the \emph{minimum frequency rule}, which requires each released group (cell) to include contributions from at least $k$ distinct individuals. In this paper, we present \textbf{DPSQL+}, a privacy-preserving SQL library that simultaneously enforces user-level $(\varepsilon,\delta)$-DP and the minimum frequency rule. DPSQL+ adopts a modular architecture consisting of: (i) a \emph{Validator} that statically restricts queries to a DP-safe subset of SQL; (ii) an \emph{Accountant} that consistently tracks cumulative privacy loss across multiple queries; and (iii) a \emph{Backend} that interfaces with various database engines, ensuring portability and extensibility. Experiments on the TPC-H benchmark demonstrate that DPSQL+ achieves practical accuracy across a wide range of analytical workloads -- from basic aggregates to quadratic statistics and join operations -- and allows substantially more queries under a fixed global privacy budget than prior libraries in our evaluation.