Julie M. Haney (National Institute of Standards and Technology, Gaithersburg, Maryland), Shanee Dawkins (National Institute of Standards and Technology, Gaithersburg, Maryland), Sandra Spickard Prettyman (Cultural Catalyst LLC, Chicago), Mary F. Theofanos (National Institute of Standards and Technology, Gaithersburg, Maryland), Kristen K. Greene (National Institute of Standards and Technology, Gaithersburg, Maryland), Kristin L. Kelly Koskey (Cultural Catalyst LLC, Chicago), Jody L. Jacobs (National Institute of Standards and Technology, Gaithersburg, Maryland)

By using cryptographic techniques, end-to-end verifiable (E2EV) voting systems have been proposed as a way to increase voter trust and confidence in elections by providing the public with direct evidence of the integrity of election systems and outcomes. However, it is unclear as to whether the path to E2EV adoption for in-person elections in the United States is feasible given the confluence of factors impacting voter trust and technology adoption. Our research addresses this gap with a first-of-its-kind interview study with 33 election experts in four areas: accessibility, cybersecurity, usability, and general elections. We found that participants’ understanding of and opinions on E2EV diverged. While E2EV was lauded by some for increased security and transparency, others described it does not address major challenges to voter trust in U.S. elections and might actually have a negative impact due to complexity and limitations. Overall, participants recognized that the feasibility of widescale E2EV adoption hinges on not just the strength and security of the technology, but also on consideration of the people and process issues surrounding it. Based on our results, we offer suggestions for future work towards informing decisions about whether to adopt E2EV systems more widely.

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Jihye Kim (Research Institute CODE, University of the Bundeswehr Munich)

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Haohuang Wen (The Ohio State University and SE-RAN.ai), Vinod Yegneswaran (SRI and SE-RAN.ai), Phillip Porras (SRI and SE-RAN.ai), Ashish Gehani (SRI and SE-RAN.ai), Prakhar Sharma (SRI and SE-RAN.ai), Zhiqiang Lin (The Ohio State University and SE-RAN.ai)

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Alexandra Xinran Li (Carnegie Mellon University), Tian Wang (University of Illinois Urbana-Champaign), Yu-Ju Yang (University of Illinois Urbana-Champaign), Miguel Rivera-Lanas (Carnegie Mellon University), Debeshi Ghosh (Carnegie Mellon University), Hana Habib (Carnegie Mellon University), Lorrie Cranor (Carnegie Mellon University), Norman Sadeh (Carnegie Mellon University)

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