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

• URL: http://arxiv.org/abs/2303.00738v1
• Date: Wed, 1 Mar 2023 18:53:25 GMT
• Title: What Are the Chances? Explaining the Epsilon Parameter in Differential Privacy
• Authors: Priyanka Nanayakkara, Mary Anne Smart, Rachel Cummings, Gabriel Kaptchuk, Elissa Redmiles
• Abstract summary: Differential privacy (DP) is a mathematical privacy notion increasingly deployed across government and industry. We develop three methods to convey probabilistic DP guarantees to end users. We find that odds-based explanation methods are more effective than output-based methods.
• Score: 17.201862983773662
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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, $\epsilon$. Prior work in health and computational science finds that people struggle to reason about probabilistic risks. Yet, communicating the implications of $\epsilon$ 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 $\epsilon$. Further, when offered information about $\epsilon$, 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 $\epsilon$ values: as privacy protections weaken, respondents are less likely to share data.

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