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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TinyML meets IoBT against Sensor Hacking

Raushan Kumar Singh (IIT Ropar), Sudeepta Mishra (IIT Ropar)

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Leaking the Privacy of Groups and More: Understanding Privacy...

Jiangrong Wu (Sun Yat-sen University), Yuhong Nan (Sun Yat-sen University), Luyi Xing (Indiana University Bloomington), Jiatao Cheng (Sun Yat-sen University), Zimin Lin (Alibaba Group), Zibin Zheng (Sun Yat-sen University), Min Yang (Fudan University)

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Does This App Respect My Privacy? Design and Evaluation...

Oksana Kulyk (Karlsruhe Institute of Technology); Paul Gerber, Karola Marky, Christopher Beckmann (Technische Universität Darmstadt); Melanie Volkamer (Karlsruhe Institute of Technology)

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WIP: A First Look At Employing Large Multimodal Models...

Mohammed Aldeen, Pedram MohajerAnsari, Jin Ma, Mashrur Chowdhury, Long Cheng, Mert D. Pesé (Clemson University)

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