可用的差分隐私：PSI的案例研究
Usable Differential Privacy: A Case Study with PSI
摘要：
差分隐私是一个很有前途的框架，用于解决共享敏感数据集供他人分析时的隐私问题。然而，差分隐私是一个高度技术性的领域，当前的部署通常需要专家编写代码，调整参数，并优化隐私和统计发布准确性之间的权衡。为了使差分隐私实现其广泛影响的潜力，重要的是设计使普通数据所有者和分析师能够使用差分隐私的可用系统。PSI是为此目的而设计的工具，允许研究人员发布有关其数据集的有用的差异化隐私统计信息，而无需成为计算机科学，统计学或隐私方面的专家。我们对PSI进行了彻底的可用性研究，以测试它是否实现了非专家可用性的目标。可用性测试阐明了PSI的哪些功能是最用户友好的，并促使我们改进工具中引起混淆的方面。该测试还强调了一些设计差分隐私可用系统的一般原则和经验教训，我们将深入讨论。
英文摘要：
Differential privacy is a promising framework for addressing the privacy concerns in sharing sensitive datasets for others to analyze. However differential privacy is a highly technical area and current deployments often require experts to write code, tune parameters, and optimize the trade-off between the privacy and accuracy of statistical releases. For differential privacy to achieve its potential for wide impact, it is important to design usable systems that enable differential privacy to be used by ordinary data owners and analysts. PSI is a tool that was designed for this purpose, allowing researchers to release useful differentially private statistical information about their datasets without being experts in computer science, statistics, or privacy. We conducted a thorough usability study of PSI to test whether it accomplishes its goal of usability by non-experts. The usability test illuminated which features of PSI are most user-friendly and prompted us to improve aspects of the tool that caused confusion. The test also highlighted some general principles and lessons for designing usable systems for differential privacy, which we discuss in depth.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
