Do I Get the Privacy I Need? Benchmarking Utility in Differential Privacy Libraries

TL;DR

This paper benchmarks five open-source differential privacy libraries, comparing their utility, features, and scalability across common analytics queries to guide practitioners, developers, and researchers.

Contribution

It provides a comprehensive qualitative and quantitative comparison of leading differential privacy libraries, highlighting strengths, weaknesses, and open challenges.

Findings

Libraries offer similar utility in most scenarios

Significant differences exist in features and capabilities

No single library excels in all evaluated areas

Abstract

An increasing number of open-source libraries promise to bring differential privacy to practice, even for non-experts. This paper studies five libraries that offer differentially private analytics: Google DP, SmartNoise, diffprivlib, diffpriv, and Chorus. We compare these libraries qualitatively (capabilities, features, and maturity) and quantitatively (utility and scalability) across four analytics queries (count, sum, mean, and variance) executed on synthetic and real-world datasets. We conclude that these libraries provide similar utility (except in some notable scenarios). However, there are significant differences in the features provided, and we find that no single library excels in all areas. Based on our results, we provide guidance for practitioners to help in choosing a suitable library, guidance for library designers to enhance their software, and guidance for researchers on open challenges in differential privacy tools for non-experts.