Across academia, government, and industry, data stewards are facing increasing pressure to make datasets more openly accessible for researchers while also protecting the privacy of data subjects. Differential privacy (DP) is one promising way to offer privacy along with open access, but further inquiry is needed into the tensions between DP and data science. In this study, we conduct interviews with 19 data practitioners who are non-experts in DP as they use a DP data analysis prototype to release privacy-preserving statistics about sensitive data, in order to understand perceptions, challenges, and opportunities around using DP. We find that while DP is promising for providing wider access to sensitive datasets, it also introduces challenges into every stage of the data science workflow. We identify ethics and governance questions that arise when socializing data scientists around new privacy constraints and offer suggestions to better integrate DP and data science.

翻译：在学术界、政府机构和工业界，数据管理者面临着越来越大的压力，既要让数据集对研究人员更开放地访问，又要保护数据主体的隐私。差分隐私（DP）是一种在提供开放访问的同时保障隐私的有效方法，但差分隐私与数据科学之间的张力仍有待深入研究。在本研究中，我们对19位非DP专家的数据从业者进行了访谈，让他们使用DP数据分析原型发布关于敏感数据的隐私保护统计结果，以了解他们对使用DP的认知、挑战和机遇。我们发现，虽然DP在促进敏感数据集的更广泛访问方面前景可观，但它也给数据科学工作流的每个阶段都带来了挑战。我们指出了在让数据科学家适应新的隐私约束时出现的伦理和治理问题，并提出了更好地整合DP与数据科学的建议。