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

arXiv: https://arxiv.org/abs/2110.06452 · HTML: https://mlsanity.com/paper/2110.06452/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Oct 2021.

## Built on by (most cited first, 17 of 17)

- [Position: Considerations for Differentially Private Learning with Large-Scale Public Pretraining](https://mlsanity.com/paper/2212.06470/) (2022)
- [Training Data Extraction From Pre-trained Language Models: A Survey](https://mlsanity.com/paper/2305.16157/) (2023)
- [Have it your way: Individualized Privacy Assignment for DP-SGD](https://mlsanity.com/paper/2303.17046/) (2023)
- [The Inadequacy of Similarity-based Privacy Metrics: Privacy Attacks against "Truly Anonymous" Synthetic Datasets](https://mlsanity.com/paper/2312.05114/) (2023)
- [The Elusive Pursuit of Reproducing PATE-GAN: Benchmarking, Auditing, Debugging](https://mlsanity.com/paper/2406.13985/) (2024)
- [Differentially Private Graph Classification with GNNs](https://mlsanity.com/paper/2202.02575/) (2022)
- [SoK: Differential Privacy on Graph-Structured Data](https://mlsanity.com/paper/2203.09205/) (2022)
- [Attack-Aware Noise Calibration for Differential Privacy](https://mlsanity.com/paper/2407.02191/) (2024)
- [When Synthetic Data Met Regulation](https://mlsanity.com/paper/2307.00359/) (2023)
- [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)
- [Video-DPRP: A Differentially Private Approach for Visual Privacy-Preserving Video Human Activity Recognition](https://mlsanity.com/paper/2503.02132/) (2025)
- [Empirical Privacy Variance](https://mlsanity.com/paper/2503.12314/) (2025)
- [Investigating User Perspectives on Differentially Private Text Privatization](https://mlsanity.com/paper/2503.09338/) (2025)
- [An Interactive Framework for Finding the Optimal Trade-off in Differential Privacy](https://mlsanity.com/paper/2509.04290/) (2025)
- [Your Privacy Depends on Others: Collusion Vulnerabilities in Individual Differential Privacy](https://mlsanity.com/paper/2601.12922/) (2026)
- [Rethinking Anonymity Claims in Synthetic Data Generation: A Model-Centric Privacy Attack Perspective](https://mlsanity.com/paper/2601.22434/) (2026)
- [Performative Privacy: When Differential Privacy Maximizes Utility](https://mlsanity.com/paper/2608.28198/) (2026)

## 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.