Towards Effective Differential Privacy Communication for Users' Data Sharing Decision and Comprehension
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Abstract (arXiv)
Differential privacy protects an individual's privacy by perturbing data on an aggregated level (DP) or individual level (LDP). We report four online human-subject experiments investigating the effects of using different approaches to communicate differential privacy techniques to laypersons in a health app data collection setting. Experiments 1 and 2 investigated participants' data disclosure decisions for low-sensitive and high-sensitive personal information when given different DP or LDP descriptions. Experiments 3 and 4 uncovered reasons behind participants' data sharing decisions, and examined participants' subjective and objective comprehensions of these DP or LDP descriptions. When shown descriptions that explain the implications instead of the definition/processes of DP or LDP technique, participants demonstrated better comprehension and showed more willingness to share information with LDP than with DP, indicating their understanding of LDP's stronger privacy guarantee compared with DP.
Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). First posted on arXiv 31 Mar 2020.
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Citing papers per year: 2022: 2; 2023: 3; 2024: 0; 2025: 3; 2026: 1.
9 papers.
- 2023Have it your way: Individualized Privacy Assignment for DP-SGDcited by 21
- 2022Individualized PATE: Differentially Private Machine Learning with Individual Privacy Guaranteescited by 10
- 2022Private, Efficient, and Accurate: Protecting Models Trained by Multi-party Learning with Differential Privacycited by 7
- 2025Position: Certified Robustness Does Not (Yet) Imply Model Securitycited by 4
- 2023To share or not to share: What risks would laypeople accept to give sensitive data to differentially-private NLP systems?cited by 3
- 2025Empirical Privacy Variancecited by 3
- 2023DP-DCAN: Differentially Private Deep Contrastive Autoencoder Network for Single-cell Clusteringcited by 1
- 2025Investigating User Perspectives on Differentially Private Text Privatizationcited by 1
- 2026Performative Privacy: When Differential Privacy Maximizes Utilitycited by 0
- 2026Performative Privacy: When Differential Privacy Maximizes Utility28 Aug 2026
- 2025Position: Certified Robustness Does Not (Yet) Imply Model Security16 Jun 2025
- 2025Empirical Privacy Variance16 Mar 2025
- 2025Investigating User Perspectives on Differentially Private Text Privatization12 Mar 2025
- 2023DP-DCAN: Differentially Private Deep Contrastive Autoencoder Network for Single-cell Clustering6 Nov 2023
- 2023To share or not to share: What risks would laypeople accept to give sensitive data to differentially-private NLP systems?13 Jul 2023
- 2023Have it your way: Individualized Privacy Assignment for DP-SGD29 Mar 2023
- 2022Private, Efficient, and Accurate: Protecting Models Trained by Multi-party Learning with Differential Privacy18 Aug 2022
- 2022Individualized PATE: Differentially Private Machine Learning with Individual Privacy Guarantees21 Feb 2022