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.

翻译：差分隐私（DP）已成为隐私保护数据分析的黄金标准，但在实际数据集和系统中实现差分隐私仍具有挑战性。近期开发的差分隐私工具旨在简化差分隐私的实现过程，但针对这些工具可用性的研究仍然有限。通过对24名具有不同差分隐私背景知识的美国数据从业者进行可用性研究，我们评估了四种基于Python的开源差分隐私工具：DiffPrivLib、Tumult Analytics、PipelineDP和OpenDP。我们的研究结果表明，使用本研究中涉及的差分隐私工具可能有助于差分隐私初学者更好地理解差分隐私；应用程序编程接口（API）设计和文档对于成功实现差分隐私至关重要；用户满意度与参与者使用这些工具完成研究任务的表现密切相关。我们提出了基于实证的建议，以改进差分隐私工具的可用性，从而促进差分隐私的更广泛采用。