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arXiv · 2309.13506

Evaluating the Usability of Differential Privacy Tools with Data Practitioners

Ivoline C. Ngong·Brad Stenger·Joseph P. Near·Yuanyuan Feng
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Abstract

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.

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Ivoline C. Ngong, Brad Stenger, Joseph P. Near, Yuanyuan Feng. 2024-08-13. Evaluating the Usability of Differential Privacy Tools with Data Practitioners. https://arxiv.org/abs/2309.13506

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