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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Faster and Better: Detecting Vulnerabilities in Linux-based IoT Firmware...

Zicong Gao (State Key Laboratory of Mathematical Engineering and Advanced Computing), Chao Zhang (Tsinghua University), Hangtian Liu (State Key Laboratory of Mathematical Engineering and Advanced Computing), Wenhou Sun (Tsinghua University), Zhizhuo Tang (State Key Laboratory of Mathematical Engineering and Advanced Computing), Liehui Jiang (State Key Laboratory of Mathematical Engineering and Advanced Computing), Jianjun Chen (Tsinghua…

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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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Towards Automated Regulation Analysis for Effective Privacy Compliance

Sunil Manandhar (IBM T.J. Watson Research Center), Kapil Singh (IBM T.J. Watson Research Center), Adwait Nadkarni (William & Mary)

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