McCorry, Patrick, Shahandashti, Siamak F. orcid.org/0000-0002-5284-6847 and Hao, Feng (2017) A Smart Contract for Boardroom Voting with Maximum Voter Privacy. In: Kiayias, Aggelos, (ed.) Financial Cryptography and Data Security - 21st International Conference, FC 2017, Revised Selected Papers:21st International Conference, FC 2017, Sliema, Malta, April 3-7, 2017, Revised Selected Papers. Financial Cryptography and Data Security, 03-07 Apr 2017 Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) . Springer , MLT , pp. 357-375.
Abstract
We present the first implementation of a decentralised and self-tallying internet voting protocol with maximum voter privacy using the Blockchain. The Open Vote Network is suitable for boardroom elections and is written as a smart contract for Ethereum. Unlike previously proposed Blockchain e-voting protocols, this is the first implementation that does not rely on any trusted authority to compute the tally or to protect the voter’s privacy. Instead, the Open Vote Network is a self-tallying protocol, and each voter is in control of the privacy of their own vote such that it can only be breached by a full collusion involving all other voters. The execution of the protocol is enforced using the consensus mechanism that also secures the Ethereum blockchain. We tested the implementation on Ethereum’s official test network to demonstrate its feasibility. Also, we provide a financial and computational breakdown of its execution cost.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © Springer, 2017. This is an author-produced version of the published paper. Uploaded in accordance with the publisher’s self-archiving policy. Further copying may not be permitted; contact the publisher for details |
Keywords: | electronic voting,blockchain,smart contracts,Ethereum |
Dates: |
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Institution: | The University of York |
Academic Units: | The University of York > Faculty of Sciences (York) > Computer Science (York) |
Depositing User: | Pure (York) |
Date Deposited: | 23 Jun 2017 14:15 |
Last Modified: | 16 Oct 2024 10:57 |
Published Version: | https://doi.org/10.1007/978-3-319-70972-7_20 |
Status: | Published |
Publisher: | Springer |
Series Name: | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
Identification Number: | 10.1007/978-3-319-70972-7_20 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:117996 |