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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Differentially Private Dataset Condensation

Tianhang Zheng (University of Missouri-Kansas City), Baochun Li (University of Toronto)

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BGP-iSec: Improved Security of Internet Routing Against Post-ROV Attacks

Cameron Morris (University of Connecticut), Amir Herzberg (University of Connecticut), Bing Wang (University of Connecticut), Samuel Secondo (University of Connecticut)

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IRRedicator: Pruning IRR with RPKI-Valid BGP Insights

Minhyeok Kang (Seoul National University), Weitong Li (Virginia Tech), Roland van Rijswijk-Deij (University of Twente), Ted "Taekyoung" Kwon (Seoul National University), Taejoong Chung (Virginia Tech)

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