Don't Look at the Data! How Differential Privacy Reconfigures the Practices of Data Science

Abstract

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.

References in corpus (5)

• RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response
• "I need a better description'': An Investigation Into User Expectations For Differential Privacy
• Exploring Design and Governance Challenges in the Development of Privacy-Preserving Computation
• Differential Perspectives: Epistemic Disconnects Surrounding the US Census Bureau's Use of Differential Privacy
• Differential Privacy for Government Agencies -- Are We There Yet?

Cited by in corpus (2)

• What to Consider When Considering Differential Privacy for Policy
• "I inherently just trust that it works": Investigating Mental Models of Open-Source Libraries for Differential Privacy