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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Cyclops: Binding a Vehicle’s Digital Identity to its Physical...

Lewis William Koplon, Ameer Ghasem Nessaee, Alex Choi (University of Arizona, Tucson), Andres Mentoza (New Mexico State University, Las Cruces), Michael Villasana, Loukas Lazos, Ming Li (University of Arizona, Tucson)

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MASTERKEY: Automated Jailbreaking of Large Language Model Chatbots

Gelei Deng (Nanyang Technological University), Yi Liu (Nanyang Technological University), Yuekang Li (University of New South Wales), Kailong Wang (Huazhong University of Science and Technology), Ying Zhang (Virginia Tech), Zefeng Li (Nanyang Technological University), Haoyu Wang (Huazhong University of Science and Technology), Tianwei Zhang (Nanyang Technological University), Yang Liu (Nanyang Technological University)

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Scenario-Driven Assessment of Cyber Risk Perception at the Security...

Simon Parkin (TU Delft), Kristen Kuhn, Siraj Ahmed Shaikh (Coventry University)

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Pencil: Private and Extensible Collaborative Learning without the Non-Colluding...

Xuanqi Liu (Tsinghua University), Zhuotao Liu (Tsinghua University), Qi Li (Tsinghua University), Ke Xu (Tsinghua University), Mingwei Xu (Tsinghua University)

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