Interpreting Differential Privacy in Terms of Disclosure Risk

TL;DR

This paper establishes new relationships between differential privacy and disclosure risk measures, aiding both experts and non-experts in understanding, interpreting, and applying DP guarantees effectively.

Contribution

It introduces novel links between differential privacy and disclosure risk, enhancing interpretability and practical decision-making in privacy-preserving data analysis.

Findings

Relationships between DP and disclosure risk measures

Guidelines for explaining DP guarantees to non-experts

Methods for interpreting DP composition and selecting privacy parameters

Abstract

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.