Automatic Margin Computation for Risk-Limiting Audits
- Bernhard Beckert,
- Michael Kirsten,
- Vladimir Klebanov,
- Karlsruhe Institute of Technology
Research Output:
Conference Article in Proceeding or Book/Report chapter
Article in proceedings
Peer-reviewOpen access
Publication Information
Output type
Research Output:
Conference Article in Proceeding or Book/Report chapter
Article in proceedings
Peer-reviewOriginal language
EnglishPages from-to (Number of pages)
Pages 18-35Publication milestones
- Published - 2017
Publication status
Published - 2017
Publisher
Springer, United States, GermanyBook series
- Book series name: Lecture Notes in Computer Science
Volume: 10141
ISSN: 0302-9743
ISBN (Print)
978-3-319-52239-5ISBN (Electronic)
978-3-319-52240-1Publication IDs
- Scopus: 85011621069
Host publication title
First International Joint Conference, E-Vote-ID 2016, Bregenz, Austria, October 18-21, 2016, ProceedingsAbstract
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.
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.
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Accepted author manuscript, 501.15 KB
Accepted author manuscript
