A formal framework for quantifying voter-controlled privacy

H.L. Jonker, S. Mauw, J. Pang

    Research output: Contribution to journalArticleAcademicpeer-review

    11 Citations (Scopus)
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    Abstract

    Privacy is a necessary requirement for voting. Without privacy, voters can be forced to vote in specific ways, and the forcing party can check their compliance. But offering privacy does not suffice: if a voter can reduce her privacy, an attacker can force her to do so. In this paper, we distinguish various ways that a voter can communicate with the intruder to reduce her privacy and classify them according to their ability to reduce the privacy of a voter. We develop a framework combining knowledge reasoning and trace equivalences to formally model voting protocols and define voter-controlled privacy. Our framework is quantitative, in the sense that it defines a measure for the privacy of a voter. Therefore, the framework can precisely measure the level of privacy for a voter for each of the identified privacy-reduction classes. The quantification allows our framework to capture receipts that reduce, but not nullify, the privacy of the voter.
    Original languageEnglish
    Pages (from-to)89-105
    JournalJournal of Algorithms
    Volume64
    Issue number2-3
    DOIs
    Publication statusPublished - 2009

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