@InProceedings{BeckertKirstenEA2017, author = {Bernhard Beckert and Michael Kirsten and Vladimir Klebanov and Carsten Sch{\"u}rmann}, title = {Automatic Margin Computation for Risk-Limiting Audits}, booktitle = {First International Joint Conference on Electronic Voting – formerly known as EVOTE and VoteID (E-Vote-ID 2016)}, editor = {Robert Krimmer and Melanie Volkamer and Jordi Barrat and Josh Benaloh and Nicole J. Goodman and Peter Y. A. Ryan and Vanessa Teague}, publisher = {Springer}, series = {LNCS}, pages = {18--35}, volume = {10141}, month = jan, year = {2017}, doi = {10.1007/978-3-319-52240-1_2}, abstract = {A risk-limiting audit is a statistical method to create confidence in the correctness of an election result by checking samples of paper ballots. In order to perform an audit, one usually needs to know what the election margin is, i.e., the number of votes that would need to be changed in order to change the election outcome. \newline In this paper, we present a fully automatic method for computing election margins. It is based on the program analysis technique of bounded model checking to analyse the implementation of the election function. The method can be applied to arbitrary election functions without understanding the actual computation of the election result or without even intuitively knowing how the election function works. \newline We have implemented our method based on the model checker CBMC; and we present a case study demonstrating that it can be applied to real-world elections.}, venue = {Lochau / Bregenz, Austria}, eventdate = {2017-10-18/2017-10-21} }
Automatic Margin Computation for Risk-Limiting Audits
Author(s): | Bernhard Beckert, Michael Kirsten, Vladimir Klebanov, and Carsten Schürmann |
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In: | First International Joint Conference on Electronic Voting – formerly known as EVOTE and VoteID (E-Vote-ID 2016) |
Publisher: | Springer |
Series: | LNCS |
Volume: | 10141 |
Year: | 2017 |
Pages: | 18-35 |
PDF: | Preprint |
DOI: | 10.1007/978-3-319-52240-1_2 |
Abstract
A risk-limiting audit is a statistical method to create confidence
in the correctness of an election result by checking samples of
paper ballots. In order to perform an audit, one usually needs to know
what the election margin is, i.e., the number of votes that would need
to be changed in order to change the election outcome.
In this paper, we present a fully automatic method for computing
election margins. It is based on the program analysis technique of
bounded model checking to analyse the implementation of the election
function. The method can be applied to arbitrary election functions
without understanding the actual computation of the election result or
without even intuitively knowing how the election function works.
We have implemented our method based on the model checker CBMC; and
we present a case study demonstrating that it can be applied to
real-world elections.