# What Are the Chances? Explaining the Epsilon Parameter in Differential Privacy

arXiv: https://arxiv.org/abs/2303.00738 · HTML: https://mlsanity.com/paper/2303.00738/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Mar 2023.

## Built on by (most cited first, 14 of 14)

- [Unifying Re-Identification, Attribute Inference, and Data Reconstruction Risks in Differential Privacy](https://mlsanity.com/paper/2507.06969/) (2025)
- [PILLAR: How to make semi-private learning more effective](https://mlsanity.com/paper/2306.03962/) (2023)
- [Attack-Aware Noise Calibration for Differential Privacy](https://mlsanity.com/paper/2407.02191/) (2024)
- [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)
- [Setting varepsilon is not the Issue in Differential Privacy](https://mlsanity.com/paper/2511.06305/) (2025)
- [Understanding Disclosure Risk in Differential Privacy with Applications to Noise Calibration and Auditing (Extended Version)](https://mlsanity.com/paper/2603.12142/) (2026)
- [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)
- [Rethinking Anonymity Claims in Synthetic Data Generation: A Model-Centric Privacy Attack Perspective](https://mlsanity.com/paper/2601.22434/) (2026)
- [From Private to Public: Benchmarking GANs in the Context of Private Time Series Classification](https://mlsanity.com/paper/2303.15916/) (2023)
- [Position: Challenges and Opportunities for Differential Privacy in the U.S. Federal Government](https://mlsanity.com/paper/2410.16423/) (2024)
- [Preempting Text Sanitization Utility in Resource-Constrained Privacy-Preserving LLM Interactions](https://mlsanity.com/paper/2411.11521/) (2024)
- [SA-ADP: Sensitivity-Aware Adaptive Differential Privacy for Large Language Models](https://mlsanity.com/paper/2512.01748/) (2025)

## Abstract (arXiv)

Differential privacy (DP) is a mathematical privacy notion increasingly deployed across government and industry. With DP, privacy protections are probabilistic: they are bounded by the privacy budget parameter, ε. Prior work in health and computational science finds that people struggle to reason about probabilistic risks. Yet, communicating the implications of ε to people contributing their data is vital to avoiding privacy theater -- presenting meaningless privacy protection as meaningful -- and empowering more informed data-sharing decisions. Drawing on best practices in risk communication and usability, we develop three methods to convey probabilistic DP guarantees to end users: two that communicate odds and one offering concrete examples of DP outputs. We quantitatively evaluate these explanation methods in a vignette survey study (n=963) via three metrics: objective risk comprehension, subjective privacy understanding of DP guarantees, and self-efficacy. We find that odds-based explanation methods are more effective than (1) output-based methods and (2) state-of-the-art approaches that gloss over information about ε. Further, when offered information about ε, respondents are more willing to share their data than when presented with a state-of-the-art DP explanation; this willingness to share is sensitive to ε values: as privacy protections weaken, respondents are less likely to share data.