# Qrlew: Rewriting SQL into Differentially Private SQL

arXiv: https://arxiv.org/abs/2401.06273 · HTML: https://mlsanity.com/paper/2401.06273/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 34 references that are in the map.
- First posted on arXiv 11 Jan 2024.

## Builds on (most cited first, 12 of 12)

- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Winning the NIST Contest: A scalable and general approach to differentially private synthetic data](https://mlsanity.com/paper/2108.04978/) (2021)
- [Diffprivlib: The IBM Differential Privacy Library](https://mlsanity.com/paper/1907.02444/) (2019)
- [Learning discrete distributions: user vs item-level privacy](https://mlsanity.com/paper/2007.13660/) (2020)
- [Differentially Private SQL with Bounded User Contribution](https://mlsanity.com/paper/1909.01917/) (2019)
- [DP-TBART: A Transformer-based Autoregressive Model for Differentially Private Tabular Data Generation](https://mlsanity.com/paper/2307.10430/) (2023)
- [Widespread Underestimation of Sensitivity in Differentially Private Libraries and How to Fix It](https://mlsanity.com/paper/2207.10635/) (2022)
- [Privately generating tabular data using language models](https://mlsanity.com/paper/2306.04803/) (2023)
- [Chorus: a Programming Framework for Building Scalable Differential Privacy Mechanisms](https://mlsanity.com/paper/1809.07750/) (2018)
- [Tumult Analytics: a robust, easy-to-use, scalable, and expressive framework for differential privacy](https://mlsanity.com/paper/2212.04133/) (2022)
- [Generative Modeling of Complex Data](https://mlsanity.com/paper/2202.02145/) (2022)
- [Identifying Participants in the Personal Genome Project by Name (A Re-identification Experiment)](https://mlsanity.com/paper/1304.7605/) (2013)

## Built on by (most cited first, 1 of 1)

- [DPSQL+: A Differentially Private SQL Library with a Minimum Frequency Rule](https://mlsanity.com/paper/2602.22699/) (2026)

## Abstract (arXiv)

This paper introduces Qrlew, an open source library that can parse SQL queries into Relations -- an intermediate representation -- that keeps track of rich data types, value ranges, and row ownership; so that they can easily be rewritten into differentially-private equivalent and turned back into SQL queries for execution in a variety of standard data stores. With Qrlew, a data practitioner can express their data queries in standard SQL; the data owner can run the rewritten query without any technical integration and with strong privacy guarantees on the output; and the query rewriting can be operated by a privacy-expert who must be trusted by the owner, but may belong to a separate organization.