Cryptology ePrint Archive: Report 2021/392

How Private Are Commonly-Used Voting Rules?

Ao Liu and Yun Lu and Lirong Xia and Vassilis Zikas

Abstract: 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.

Category / Keywords: applications / differential privacy, distributional differential privacy, voting, rank aggregation, social choice, generalized scoring rules

Original Publication (in the same form): Uncertainty in Artificial Intelligence (UAI) 2020

Date: received 24 Mar 2021

Contact author: yunlu mail at gmail com

Available format(s): PDF | BibTeX Citation

Version: 20210327:071421 (All versions of this report)

Short URL: ia.cr/2021/392