# Towards Effective Differential Privacy Communication for Users' Data Sharing Decision and Comprehension

arXiv: https://arxiv.org/abs/2003.13922 · HTML: https://mlsanity.com/paper/2003.13922/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Mar 2020.

## Built on by (most cited first, 9 of 9)

- [Have it your way: Individualized Privacy Assignment for DP-SGD](https://mlsanity.com/paper/2303.17046/) (2023)
- [Individualized PATE: Differentially Private Machine Learning with Individual Privacy Guarantees](https://mlsanity.com/paper/2202.10517/) (2022)
- [Private, Efficient, and Accurate: Protecting Models Trained by Multi-party Learning with Differential Privacy](https://mlsanity.com/paper/2208.08662/) (2022)
- [Position: Certified Robustness Does Not (Yet) Imply Model Security](https://mlsanity.com/paper/2506.13024/) (2025)
- [To share or not to share: What risks would laypeople accept to give sensitive data to differentially-private NLP systems?](https://mlsanity.com/paper/2307.06708/) (2023)
- [Empirical Privacy Variance](https://mlsanity.com/paper/2503.12314/) (2025)
- [DP-DCAN: Differentially Private Deep Contrastive Autoencoder Network for Single-cell Clustering](https://mlsanity.com/paper/2311.03410/) (2023)
- [Investigating User Perspectives on Differentially Private Text Privatization](https://mlsanity.com/paper/2503.09338/) (2025)
- [Performative Privacy: When Differential Privacy Maximizes Utility](https://mlsanity.com/paper/2608.28198/) (2026)

## 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.