"I need a better description'': An Investigation Into User Expectations For Differential Privacy
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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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Citing papers per year: 2022: 3; 2023: 5; 2024: 2; 2025: 4; 2026: 3.
17 papers.
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- 2022Position: Considerations for Differentially Private Learning with Large-Scale Public Pretrainingcited by 86
- 2023Training Data Extraction From Pre-trained Language Models: A Surveycited by 43
- 2023Have it your way: Individualized Privacy Assignment for DP-SGDcited by 21
- 2023The Inadequacy of Similarity-based Privacy Metrics: Privacy Attacks against "Truly Anonymous" Synthetic Datasetscited by 14
- 2022Differentially Private Graph Classification with GNNscited by 12
- 2024The Elusive Pursuit of Reproducing PATE-GAN: Benchmarking, Auditing, Debuggingcited by 12
- 2022SoK: Differential Privacy on Graph-Structured Datacited by 9
- 2024Attack-Aware Noise Calibration for Differential Privacycited by 6
- 2023When Synthetic Data Met Regulationcited by 3
- 2023To share or not to share: What risks would laypeople accept to give sensitive data to differentially-private NLP systems?cited by 3
- 2025Video-DPRP: A Differentially Private Approach for Visual Privacy-Preserving Video Human Activity Recognitioncited by 3
- 2025Empirical Privacy Variancecited by 3
5 more
- 2025Investigating User Perspectives on Differentially Private Text Privatizationcited by 1
- 2025An Interactive Framework for Finding the Optimal Trade-off in Differential Privacycited by 1
- 2026Your Privacy Depends on Others: Collusion Vulnerabilities in Individual Differential Privacycited by 1
- 2026Rethinking Anonymity Claims in Synthetic Data Generation: A Model-Centric Privacy Attack Perspectivecited by 1
- 2026Performative Privacy: When Differential Privacy Maximizes Utilitycited by 0
- 2026Performative Privacy: When Differential Privacy Maximizes Utility28 Aug 2026
- 2026Rethinking Anonymity Claims in Synthetic Data Generation: A Model-Centric Privacy Attack Perspective30 Jan 2026
- 2026Your Privacy Depends on Others: Collusion Vulnerabilities in Individual Differential Privacy19 Jan 2026
- 2025An Interactive Framework for Finding the Optimal Trade-off in Differential Privacy4 Sep 2025
- 2025Empirical Privacy Variance16 Mar 2025
- 2025Investigating User Perspectives on Differentially Private Text Privatization12 Mar 2025
- 2025Video-DPRP: A Differentially Private Approach for Visual Privacy-Preserving Video Human Activity Recognition3 Mar 2025
- 2024Attack-Aware Noise Calibration for Differential Privacy2 Jul 2024
- 2024The Elusive Pursuit of Reproducing PATE-GAN: Benchmarking, Auditing, Debugging20 Jun 2024
- 2023The Inadequacy of Similarity-based Privacy Metrics: Privacy Attacks against "Truly Anonymous" Synthetic Datasets8 Dec 2023
- 2023To share or not to share: What risks would laypeople accept to give sensitive data to differentially-private NLP systems?13 Jul 2023
- 2023When Synthetic Data Met Regulation1 Jul 2023
5 more
- 2023Training Data Extraction From Pre-trained Language Models: A Survey25 May 2023
- 2023Have it your way: Individualized Privacy Assignment for DP-SGD29 Mar 2023
- 2022Position: Considerations for Differentially Private Learning with Large-Scale Public Pretraining13 Dec 2022
- 2022SoK: Differential Privacy on Graph-Structured Data17 Mar 2022
- 2022Differentially Private Graph Classification with GNNs5 Feb 2022
- 2022Position: Considerations for Differentially Private Learning with Large-Scale Public Pretrainingcited by 86
- 2023Training Data Extraction From Pre-trained Language Models: A Surveycited by 43
- 2023Have it your way: Individualized Privacy Assignment for DP-SGDcited by 21
- 2023The Inadequacy of Similarity-based Privacy Metrics: Privacy Attacks against "Truly Anonymous" Synthetic Datasetscited by 14
- 2022Differentially Private Graph Classification with GNNscited by 12
- 2024The Elusive Pursuit of Reproducing PATE-GAN: Benchmarking, Auditing, Debuggingcited by 12
- 2022SoK: Differential Privacy on Graph-Structured Datacited by 9
- 2024Attack-Aware Noise Calibration for Differential Privacycited by 6
- 2023When Synthetic Data Met Regulationcited by 3
- 2023To share or not to share: What risks would laypeople accept to give sensitive data to differentially-private NLP systems?cited by 3
- 2025Video-DPRP: A Differentially Private Approach for Visual Privacy-Preserving Video Human Activity Recognitioncited by 3
- 2025Empirical Privacy Variancecited by 3
12 most cited of 17 All 17 (JSON)
- 2026Performative Privacy: When Differential Privacy Maximizes Utility28 Aug 2026
- 2026Rethinking Anonymity Claims in Synthetic Data Generation: A Model-Centric Privacy Attack Perspective30 Jan 2026
- 2026Your Privacy Depends on Others: Collusion Vulnerabilities in Individual Differential Privacy19 Jan 2026
- 2025An Interactive Framework for Finding the Optimal Trade-off in Differential Privacy4 Sep 2025
- 2025Empirical Privacy Variance16 Mar 2025
- 2025Investigating User Perspectives on Differentially Private Text Privatization12 Mar 2025
- 2025Video-DPRP: A Differentially Private Approach for Visual Privacy-Preserving Video Human Activity Recognition3 Mar 2025
- 2024Attack-Aware Noise Calibration for Differential Privacy2 Jul 2024
- 2024The Elusive Pursuit of Reproducing PATE-GAN: Benchmarking, Auditing, Debugging20 Jun 2024
- 2023The Inadequacy of Similarity-based Privacy Metrics: Privacy Attacks against "Truly Anonymous" Synthetic Datasets8 Dec 2023
- 2023To share or not to share: What risks would laypeople accept to give sensitive data to differentially-private NLP systems?13 Jul 2023
- 2023When Synthetic Data Met Regulation1 Jul 2023
12 newest of 17 All 17 (JSON)