Evaluating the Usability of Differential Privacy Tools with Data Practitioners

Differential privacy (DP) has become the gold standard in privacy-preserving data analytics, but implementing it in realworld 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 open-source Python-based DP tools: DiffPrivLib, Tumult Analytics, PipelineDP, and OpenDP. Our study results suggest that these DP tools moderately support data practitioners’ DP understanding and implementation; that Application Programming Interface (API) design and documentation are vital for successful DP implementation and user satisfaction. We provide evidence-based recommendations to improve DP tools’ usability to broaden DP adoption.