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