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

arXiv: https://arxiv.org/abs/2602.22699 · HTML: https://mlsanity.com/paper/2602.22699/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 22 references that are in the map.
- First posted on arXiv 26 Feb 2026.

## Builds on (most cited first, 10 of 10)

- [Concentrated Differential Privacy: Simplifications, Extensions, and Lower Bounds](https://mlsanity.com/paper/1605.02065/) (2016)
- [Differentially Private SQL with Bounded User Contribution](https://mlsanity.com/paper/1909.01917/) (2019)
- [Precision-based attacks and interval refining: how to break, then fix, differential privacy on finite computers](https://mlsanity.com/paper/2207.13793/) (2022)
- [Exact Privacy Analysis of the Gaussian Sparse Histogram Mechanism](https://mlsanity.com/paper/2202.01100/) (2022)
- [Plume: Differential Privacy at Scale](https://mlsanity.com/paper/2201.11603/) (2022)
- [Chorus: a Programming Framework for Building Scalable Differential Privacy Mechanisms](https://mlsanity.com/paper/1809.07750/) (2018)
- [Toward provably private analytics and insights into GenAI use](https://mlsanity.com/paper/2510.21684/) (2025)
- [Qrlew: Rewriting SQL into Differentially Private SQL](https://mlsanity.com/paper/2401.06273/) (2024)
- [Optimal Variance and Covariance Estimation under Differential Privacy in the Add-Remove Model and Beyond](https://mlsanity.com/paper/2509.04919/) (2025)
- [Experiments & Analysis of Privacy-Preserving SQL Query Sanitization Systems](https://mlsanity.com/paper/2510.13528/) (2025)

## Abstract (arXiv)

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 minimum frequency rule, which requires each released group (cell) to include contributions from at least k distinct individuals. In this paper, we present DPSQL+, a privacy-preserving SQL library that simultaneously enforces user-level (varepsilon,δ)-DP and the minimum frequency rule. DPSQL+ adopts a modular architecture consisting of: (i) a Validator that statically restricts queries to a DP-safe subset of SQL; (ii) an Accountant that consistently tracks cumulative privacy loss across multiple queries; and (iii) a 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.