Minerva– An Efficient Risk-Limiting Ballot Polling Audit


Filip Zagórski, Wroclaw University of Science and Technology; Grant McClearn and Sarah Morin, The George Washington University; Neal McBurnett; Poorvi L. Vora, The George Washington University


Evidence-based elections aim to produce trustworthy and compelling evidence of the correctness of election outcomes, enabling the detection of problems with high probability. They require a well-curated voter-verified paper trail, compliance audits, and a rigorous tabulation audit of the election outcome, known as a risk-limiting audit (RLA).

This paper focuses on ballot polling RLAs which can require that a very large sample of ballots be drawn. The main ballot polling RLA in use today, BRAVO, is designed for use when single ballots are drawn at random and a decision regarding whether to stop the audit or draw another ballot is taken after each ballot draw. But in practice, ballot polling audits draw many ballots in a single round before determining whether to stop.

Direct application of BRAVO to large rounds results in considerable inefficiency. We present MINERVA, a risk-limiting audit that addresses this problem. When compared to the BRAVO stopping rule being applied at the end of the round, for a first-round with 90% stopping probability, MINERVA halves the number of ballots required across all state margins in the 2020 US Presidential election. When compared to the BRAVO stopping rule being applied after examination of individual ballots, MINERVA reduces the number of ballots by about a quarter. MINERVA requires that round sizes are predetermined; this does not appear to be a drawback for large first rounds which have been typical choices for election officials.

Ballot-polling audits are the leading option in most states. MINERVA significantly reduces the necessary expense for contests with close margins and thus makes adopting RLAs easier. Wider adoption of RLAs is a critical step in increasing public confidence in elections.

MINERVA was used in Ohio's pilot RLA of the primaries in May 2020 in Montgomery County. We provide open-source implementations of MINERVA. The code has been integrated as an option in Arlo, the most widely-used RLA software.

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@inproceedings {274735,
author = {Filip Zag{\'o}rski and Grant McClearn and Sarah Morin and Neal McBurnett and Poorvi L. Vora},
title = {Minerva{\textendash} An Efficient {Risk-Limiting} Ballot Polling Audit},
booktitle = {30th USENIX Security Symposium (USENIX Security 21)},
year = {2021},
isbn = {978-1-939133-24-3},
pages = {3059--3076},
url = {https://www.usenix.org/conference/usenixsecurity21/presentation/zagorski},
publisher = {USENIX Association},
month = aug,

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