Imani N. S. Munyaka (University of California, San Diego), Daniel A Delgado, Juan Gilbert, Jaime Ruiz, Patrick Traynor (University of Florida)

Telephone carriers and third-party developers have created technical solutions to detect and notify consumers of spam calls. The goal of this technology is to help users make decisions about incoming calls and reduce the negative effects of spam calls on finances and daily life. Although useful, this technology has varying accuracy due to technical limitations. In this study, we conduct design interviews, a call response diary study, and an MTurk survey (N=143) to explore the relationship between warning accuracy and callee decision-making for incoming calls. Our results suggest that previous call experience can lead to incomplete mental models of how Caller ID works. Additionally, we find that false alarms and missed detection do not impact call response but can influence user expectations of the call. Since adversaries can use mismatched expectations to their advantage, we recommend using warning design characteristics that align with user expectations under detection accuracy constraints.

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COSPAS Search and Rescue Satellite Uplink: A MAC-Based Security...

Syed Khandker (New York University Abu Dhabi), Krzysztof Jurczok (Amateur Radio Operator), Christina Pöpper (New York University Abu Dhabi)

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Using Behavior Monitoring to Identify Privacy Concerns in Smarthome...

Atheer Almogbil, Momo Steele, Sofia Belikovetsky (Johns Hopkins University), Adil Inam (University of Illinois at Urbana-Champaign), Olivia Wu (Johns Hopkins University), Aviel Rubin (Johns Hopkins University), Adam Bates (University of Illinois at Urbana-Champaign)

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AnonPSI: An Anonymity Assessment Framework for PSI

Bo Jiang (TikTok Inc.), Jian Du (TikTok Inc.), Qiang Yan (TikTok Inc.)

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The Impact of Workload on Phishing Susceptibility: An Experiment

Sijie Zhuo (University of Auckland), Robert Biddle (University of Auckland and Carleton University, Ottawa), Lucas Betts, Nalin Asanka Gamagedara Arachchilage, Yun Sing Koh, Danielle Lottridge, Giovanni Russello (University of Auckland)

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