Understanding Risks of Privacy Theater with Differential Privacy
Mary Anne Smart, Dhruv Sood, Kristen Vaccaro
Abstract
Differential privacy is one of the most popular technologies in the growing area of privacy-conscious data analytics. But differential privacy, along with other privacy-enhancing technologies, may enable privacy theater. In implementations of differential privacy, certain algorithm parameters control the tradeoff between privacy protection for individuals and utility for the data collector; thus, data collectors who do not provide transparency into these parameters may obscure the limited protection offered by their implementation. Through large-scale online surveys, we investigate whether explanations of differential privacy that hide important information about algorithm parameters persuade users to share more browser history data. Surprisingly, we find that the explanations have little effect on individuals' willingness to share data. In fact, most people make up their minds about whether to share before they even learn about the privacy protection.
CCS Concepts: • Human-centered computing → Human computer interaction (HCI); User studies; • Security and privacy → Human and societal aspects of security and privacy; Usability in security and privacy.
Ask about this paper
Your agent reads all of it.
Lune indexed this paper to the last equation, along with the top-tier papers that cite it. Ask a question and the answer quotes them.
Cited by top-tier papers7
- Don't Look at the Data! How Differential Privacy Reconfigures the Practices of Data ScienceJayshree Sarathy, Sophia Song, Audrey Haque, Tania Schlatter et al.CHI 2023 · 24 citations
- Unifying Re-Identification, Attribute Inference, and Data Reconstruction Risks in Differential PrivacyBogdan Kulynych, Juan Felipe Gómez, Georgios Kaissis, Jamie Hayes et al.NeurIPS 2025 · 15 citations
- "Having Confidence in My Confidence Intervals": How Data Users Engage with Privacy-Protected Wikipedia DataHarold Triedman, Jayshree Sarathy, Priyanka Nanayakkara, Rachel Cummings et al.CHI 2026 · 2 citations
- Comprehension from Chaos: Towards Informed Consent for Private ComputationBailey Kacsmar, Vasisht Duddu, Kyle Tilbury, Blase Ur et al.CCS 2023 · 2 citations
- "I inherently just trust that it works": Investigating Mental Models of Open-Source Libraries for Differential PrivacyPatrick Song, Jayshree Sarathy, Michael Shoemate, Salil P. VadhanCSCW 2024 · 1 citation
Builds on8
- Evaluating Differentially Private Machine Learning in PracticeBargav Jayaraman, David EvansUSENIX Security 2019 · 586 citations
- Auditing Differentially Private Machine Learning: How Private is Private SGD?Matthew Jagielski, Jonathan R. Ullman, Alina OpreaNeurIPS 2020 · 354 citations
- How I Learned to be Secure: a Census-Representative Survey of Security Advice Sources and BehaviorElissa M. Redmiles, Sean Kross, Michelle L. MazurekCCS 2016 · 192 citations
- Informing the Design of a Personalized Privacy Assistant for the Internet of ThingsJessica Colnago, Yuanyuan Feng, Tharangini Palanivel, Sarah Pearman et al.CHI 2020 · 103 citations
- "At the End of the Day Facebook Does What ItWants": How Users Experience Contesting Algorithmic Content ModerationKristen Vaccaro, Christian Sandvig, Karrie KarahaliosCSCW 2020 · 79 citations
Related papers
- What Are the Chances? Explaining the Epsilon Parameter in Differential PrivacyPriyanka Nanayakkara, Mary Anne Smart, Rachel Cummings, Gabriel Kaptchuk et al.USENIX Security 2023
- "I need a better description": An Investigation Into User Expectations For Differential PrivacyRachel Cummings, Gabriel Kaptchuk, Elissa M. RedmilesCCS 2021 · 45 citations
- Towards Effective Differential Privacy Communication for Users' Data Sharing Decision and ComprehensionAiping Xiong, Tianhao Wang, Ninghui Li, Somesh JhaS&P 2020 · 72 citations
- Communicating the Privacy-Utility Trade-off: Supporting Informed Data Donation with Privacy Decision Interfaces for Differential PrivacyDaniel Franzen, Claudia Müller-Birn, Odette WegwarthCSCW 2024 · 11 citations
- The Importance of Being Earnest: Shedding Light on Johnny's (False) Sense of PrivacyWirawan Agahari, Alexandra Dirksen, Martin Johns, Mark de Reuver et al.S&P 2025
