Events
FDS Colloquium
Two-prover election audits with untrusted scanners
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Speaker: Adam Sealfon (Google) Research Scientist, Google Research (NYC) Google Research NYC Wednesday, December 4, 2024 11:30AM - 1:00PM Lunch at 11:30am in 1307
Talk at 12:00pm in 1327 Location: Yale Institute for Foundations of Data Science, Kline Tower 13th Floor, Room 1327, New Haven, CT 06511 and via Webcast: https://yale.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=2d311090-a894-4754-94d0-b22e015e11bb |
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Abstract: We introduce a new way to conduct election audits using untrusted scanners. Post-election audits perform statistical hypothesis testing to confirm election outcomes. However, existing approaches are costly and laborious for close elections—often the most important cases to audit—requiring extensive hand inspection of ballots. We instead propose automated consistency checks, augmented by manual checks of only a small number of ballots. Our protocols scan each ballot twice, shuffling the ballots between scans: a “two-scan” approach inspired by two-prover proof systems. We show that this gives strong statistical guarantees even for close elections, provided that (1) the permutation accomplished by the shuffle is unknown to the scanners and (2) the scanners cannot reliably identify a particular ballot among others cast for the same candidate.Based on joint work with Douglas Jones, Sunoo Park, and Ron Rivest. This work was performed when Adam was affiliated with MIT and UC Berkeley.
Speaker bio: Adam is a research scientist at Google Research, based in NYC. His current research focus is on differential privacy. He is also broadly interested in algorithms, cryptography and machine learning, particularly provable guarantees for robustness and privacy. Before joining Google, he completed his PhD in computer science at MIT and did a postdoc in statistics at UC Berkeley.
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