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SmartTeV—Leveraging eVoting with Smart Voting Ballots

  • Conference paper
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Intelligent Distributed Computing XIV (IDC 2021)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1026))

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Abstract

Research and commercial approaches on electronic voting (e-Voting) exist and the recently emerged blockchain technology has given this research area yet another boost. However, one may claim that the actual deployment of e-Voting is at its infancy, mainly due to election integrity issues such as vote non-tampering, voter anonymity, and vote verifiability. The integration of cryptography and blockchain technology in a single framework could provide a solid foundation for secure, anonymous and still verifiable electronic voting. The TrustedEVoting (TeV) framework is a blockchain-based eVoting approach, which wraps not only the required key elements for secure and verifiable e-Voting but also additional features such as support for re-voting and post-election vote check, among others. This paper presents a novel feature to be integrated in TeV, the smart voting ballot, that implements a ranked-choice voting by allowing the voter to cast two votes: a primary vote and an alternative vote. The latter one is utilized in the case the primary vote went to an election party that didn’t reach the threshold. In this way, a more flexible voting scheme is available to a voter, mitigating the voter reluctancy to vote for smaller parties. In order to protect the voter’s privacy by not revealing trends/preferences/statistics regarding the two votes, an intelligent mechanism is in place that provides a privacy shield for the alternative votes not being used in the voting tallying.

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Notes

  1. 1.

    For brevity reasons, only the encrypted part (with the \(PU_{district}\)) of the Vote Record is shown.

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Correspondence to Harald Gjermundrød .

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Dionysiou, I., Gjermundrød, H. (2022). SmartTeV—Leveraging eVoting with Smart Voting Ballots. In: Camacho, D., Rosaci, D., Sarné, G.M.L., Versaci, M. (eds) Intelligent Distributed Computing XIV. IDC 2021. Studies in Computational Intelligence, vol 1026. Springer, Cham. https://doi.org/10.1007/978-3-030-96627-0_35

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