mlsanityData to 7 Oct 2026

"I need a better description'': An Investigation Into User Expectations For Differential Privacy

arXiv 2110.06452 ↗First posted 13 Oct 2021cs.CY cs.CRcited by 17

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Abstract (arXiv)

Despite recent widespread deployment of differential privacy, relatively little is known about what users think of differential privacy. In this work, we seek to explore users' privacy expectations related to differential privacy. Specifically, we investigate (1) whether users care about the protections afforded by differential privacy, and (2) whether they are therefore more willing to share their data with differentially private systems. Further, we attempt to understand (3) users' privacy expectations of the differentially private systems they may encounter in practice and (4) their willingness to share data in such systems. To answer these questions, we use a series of rigorously conducted surveys (n=2424). We find that users care about the kinds of information leaks against which differential privacy protects and are more willing to share their private information when the risks of these leaks are less likely to happen. Additionally, we find that the ways in which differential privacy is described in-the-wild haphazardly set users' privacy expectations, which can be misleading depending on the deployment. We synthesize our results into a framework for understanding a user's willingness to share information with differentially private systems, which takes into account the interaction between the user's prior privacy concerns and how differential privacy is described.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). First posted on arXiv 13 Oct 2021.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 5; 2024: 2; 2025: 4; 2026: 3.

17 papers.

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  1. 2022Position: Considerations for Differentially Private Learning with Large-Scale Public Pretrainingcited by 86
  2. 2023Training Data Extraction From Pre-trained Language Models: A Surveycited by 43
  3. 2023Have it your way: Individualized Privacy Assignment for DP-SGDcited by 21
  4. 2023The Inadequacy of Similarity-based Privacy Metrics: Privacy Attacks against "Truly Anonymous" Synthetic Datasetscited by 14
  5. 2022Differentially Private Graph Classification with GNNscited by 12
  6. 2024The Elusive Pursuit of Reproducing PATE-GAN: Benchmarking, Auditing, Debuggingcited by 12
  7. 2022SoK: Differential Privacy on Graph-Structured Datacited by 9
  8. 2024Attack-Aware Noise Calibration for Differential Privacycited by 6
  9. 2023When Synthetic Data Met Regulationcited by 3
  10. 2023To share or not to share: What risks would laypeople accept to give sensitive data to differentially-private NLP systems?cited by 3
  11. 2025Video-DPRP: A Differentially Private Approach for Visual Privacy-Preserving Video Human Activity Recognitioncited by 3
  12. 2025Empirical Privacy Variancecited by 3
5 more
  1. 2025Investigating User Perspectives on Differentially Private Text Privatizationcited by 1
  2. 2025An Interactive Framework for Finding the Optimal Trade-off in Differential Privacycited by 1
  3. 2026Your Privacy Depends on Others: Collusion Vulnerabilities in Individual Differential Privacycited by 1
  4. 2026Rethinking Anonymity Claims in Synthetic Data Generation: A Model-Centric Privacy Attack Perspectivecited by 1
  5. 2026Performative Privacy: When Differential Privacy Maximizes Utilitycited by 0