1. What Are the Chances? Explaining the Epsilon Parameter in Differential Privacy
- Author
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Nanayakkara, P., Smart, M., Cummings, R., Kaptchuk, G., and Redmiles, E.
- Abstract
Differential privacy (DP) is a mathematical privacy notion increasinglydeployed across government and industry. With DP, privacy protections areprobabilistic: they are bounded by the privacy budget parameter, $\epsilon$.Prior work in health and computational science finds that people struggle toreason about probabilistic risks. Yet, communicating the implications of$\epsilon$ to people contributing their data is vital to avoiding privacytheater -- presenting meaningless privacy protection as meaningful -- andempowering more informed data-sharing decisions. Drawing on best practices inrisk communication and usability, we develop three methods to conveyprobabilistic DP guarantees to end users: two that communicate odds and oneoffering concrete examples of DP outputs. We quantitatively evaluate these explanation methods in a vignette surveystudy ($n=963$) via three metrics: objective risk comprehension, subjectiveprivacy understanding of DP guarantees, and self-efficacy. We find thatodds-based explanation methods are more effective than (1) output-based methodsand (2) state-of-the-art approaches that gloss over information about$\epsilon$. Further, when offered information about $\epsilon$, respondents aremore willing to share their data than when presented with a state-of-the-art DPexplanation; this willingness to share is sensitive to $\epsilon$ values: asprivacy protections weaken, respondents are less likely to share data.
- Published
- 2023