How Private Is Your Voting? A Framework for Comparing the Privacy of Voting Mechanisms

Ao Liu, Yun Lu, Lirong Xia, Vassilis Zikas

Research output: Contribution to conferencePaperpeer-review

Abstract

Voting privacy has received a lot of attention across several research communities. Traditionally, the cryptographic literature has focused on how to privately implement/emulate a voting mechanism. Yet, a number of recent works attempt to capture (and minimize) the amount of information one can infer from the output (rather than the implementation) of the voting mechanism. These works apply differential privacy, in short DP, techniques which noise the outcome to achieve privacy. This approach intrinsically compromises accuracy, rendering such a voting mechanism unsuitable for most realistic scenarios.
In this work we address the question of what is the inherent privacy that different voting rules achieve, without noising the result. To this end we utilize a well accepted notion of noiseless privacy introduced by Bassilyet al. [FOCS 2013] called Distributional Differential Privacy, in short DDP. We argue that under standard assumptions in the voting literature about the distribution of votes, most natural mechanisms achieve a satisfactory level of DDP, indicating that noising—and its negative side-effects for voting—is unnecessary in most cases.
We then put forth a systematic study of noiseless privacy of commonly studied of voting rules, and compare these rules with respect to their achieved privacy. Note that both DP and DDP induce (possibly loose) upper bounds on the amount of information that can be inferred, which makes them insufficient for such a task. To circumvent this, we introduce an exact notion of privacy, which requires the bound to be exact (i.e. optimal) in a well defined manner. This allows us to order different voting rules with respect to their achieved privacy. Although motivated by voting, our definitions and techniques can be generically applied to address the optimality (with respect to privacy) of general mechanisms for privacy-preserving data release
Original languageEnglish
Number of pages39
Publication statusPublished - 22 Jun 2018
Event1st Workshop on Opinion Aggregation, Dynamics, and Elicitation - Ithaca, United States
Duration: 22 Jun 201822 Jun 2018
https://sites.google.com/view/wade-workshop/home?authuser=0

Workshop

Workshop1st Workshop on Opinion Aggregation, Dynamics, and Elicitation
Abbreviated titleWADE 2018
Country/TerritoryUnited States
CityIthaca
Period22/06/1822/06/18
Internet address

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