Differential privacy (DP) has become the gold standard in privacy-preserving data analytics, but implementing it in real-world datasets and systems remains challenging. Recently developed DP tools aim to make DP implementation easier, but limited research has investigated these DP tools' usability. Through a usability study with 24 US data practitioners with varying prior DP knowledge, we evaluated the usability of four Python-based open-source DP tools: DiffPrivLib, Tumult Analytics, PipelineDP, and OpenDP. Our results suggest that using DP tools in this study may help DP novices better understand DP; that Application Programming Interface (API) design and documentation are vital for successful DP implementation; and that user satisfaction correlates with how well participants completed study tasks with these DP tools. We provide evidence-based recommendations to improve DP tools' usability to broaden DP adoption.
@article{arxiv.2309.13506,
title = {Evaluating the Usability of Differential Privacy Tools with Data Practitioners},
author = {Ivoline C. Ngong and Brad Stenger and Joseph P. Near and Yuanyuan Feng},
journal= {arXiv preprint arXiv:2309.13506},
year = {2024}
}