  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.
