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 ease data practitioners' burden in implementing DP solutions, but limited research has investigated these DP tools' usability. Through a usability study with 24 US data practitioners with varying prior DP knowledge, we comprehensively evaluate the usability of four Python-based open-source DP tools: DiffPrivLib, Tumult Analytics, PipelineDP, and OpenDP. Our results suggest that DP tools can help novices learn DP concepts; that Application Programming Interface (API) design and documentation are vital for learnability and error prevention; and that user satisfaction highly correlates with the effectiveness of the tool. We discuss the balance between ease of use and the learning curve needed to appropriately implement DP and also provide recommendations to improve DP tools' usability to broaden adoption.

翻译：差分隐私已成为隐私保护数据分析的金标准，但在真实数据集和系统中实施仍具挑战性。近期开发的差分隐私工具旨在减轻数据从业者实施差分隐私解决方案的负担，但针对这些工具可用性的研究尚不充分。通过对24名具有不同差分隐私知识背景的美国数据从业者进行可用性研究，我们全面评估了四款基于Python的开源差分隐私工具：DiffPrivLib、Tumult Analytics、PipelineDP和OpenDP。研究结果表明：差分隐私工具可帮助初学者学习差分隐私概念；应用程序编程接口（API）设计与文档对可学习和错误预防至关重要；用户满意度与工具有效性高度相关。我们探讨了易用性与适当实施差分隐私所需学习曲线之间的平衡，并提出了改进差分隐私工具可用性以扩大采用范围的建议。