How Private Are Commonly-Used Voting Rules?
This work addresses privacy concerns in high-stakes voting scenarios for policymakers and researchers, offering a novel framework to analyze deterministic methods.
The authors tackled the problem of privacy in deterministic voting rules, showing that these rules can provide sufficient privacy under distributional differential privacy (DDP) and introducing exact privacy to compare common voting rules, with results including dichotomy theorems for generalized scoring rules.
Differential privacy has been widely applied to provide privacy guarantees by adding random noise to the function output. However, it inevitably fails in many high-stakes voting scenarios, where voting rules are required to be deterministic. In this work, we present the first framework for answering the question: "How private are commonly-used voting rules?" Our answers are two-fold. First, we show that deterministic voting rules provide sufficient privacy in the sense of distributional differential privacy (DDP). We show that assuming the adversarial observer has uncertainty about individual votes, even publishing the histogram of votes achieves good DDP. Second, we introduce the notion of exact privacy to compare the privacy preserved in various commonly-studied voting rules, and obtain dichotomy theorems of exact DDP within a large subset of voting rules called generalized scoring rules.