k-Cut: A Simple Approximately-Uniform Method for Sampling Ballots in Post-election Audits
Name
SridharRivest2019-k-cut.pdf
Description
Accepted version
Size
422.5 KB
Format
Unknown
Checksum (MD5)
909abc51cddbd615dfa74a827270db13
Author(s)
Sridhar, Mayuri
Rivest, Ronald L
Date Issued
November 2018
Journal
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Publisher
Springer International Publishing
Citation
Sridhar, M and Rivest, RL. 2018. "k-Cut: A Simple Approximately-Uniform Method for Sampling Ballots in Post-election Audits." Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 11599 LNCS.
Version
Author's final manuscript
Abstract
© 2020, International Financial Cryptography Association. We present an approximate sampling framework and discuss how risk-limiting audits can compensate for these approximations, while maintaining their “risk-limiting” properties. Our framework is general and can compensate for counting mistakes made during audits. Moreover, we present and analyze a simple approximate sampling method, “k-cut”, for picking a ballot randomly from a stack, without counting. Our method involves doing k “cuts,” each involving moving a random portion of ballots from the top to the bottom of the stack, and then picking the ballot on top. Unlike conventional methods of picking a ballot at random, k-cut does not require identification numbers on the ballots or counting many ballots per draw. We analyze how close the distribution of chosen ballots is to the uniform distribution, and design mitigation procedures. We show that k=6 cuts is enough for a risk-limiting election audit, based on empirical data, which provides a significant increase in sampling efficiency. This method has been used in pilot RLAs in Indiana and is scheduled to be used in Michigan pilot audits in December 2018.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/978-3-030-43725-1_17
