Interpreting Differential Privacy in Terms of Disclosure Risk
By: Zeki Kazan , Sagar Sharma , Wanrong Zhang and more
Potential Business Impact:
Explains how private data stays safe from snoops.
As the use of differential privacy (DP) becomes widespread, the development of effective tools for reasoning about the privacy guarantee becomes increasingly critical. In pursuit of this goal, we demonstrate novel relationships between DP and measures of statistical disclosure risk. We suggest how experts and non-experts can use these results to explain the DP guarantee, interpret DP composition theorems, select and justify privacy parameters, and identify worst-case adversary prior probabilities.
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