Meisam Mohammady (Iowa State University), Reza Arablouei (Data61, CSIRO)

We estimate vehicular traffic states from multi-modal data collected by single-loop detectors while preserving the privacy of the individual vehicles contributing to the data. To this end, we propose a novel hybrid differential privacy (DP) approach that utilizes minimal randomization to preserve privacy by taking advantage of the relevant traffic state dynamics and the concept of DP sensitivity. Through theoretical analysis and experiments with real-world data, we show that the proposed approach significantly outperforms the related baseline non-private and private approaches in terms of accuracy and privacy preservation.

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WIP: Towards Practical LiDAR Spoofing Attack against Vehicles Driving...

Ryo Suzuki (Keio University), Takami Sato (University of California, Irvine), Yuki Hayakawa, Kazuma Ikeda, Ozora Sako, Rokuto Nagata (Keio University), Qi Alfred Chen (University of California, Irvine), Kentaro Yoshioka (Keio University)

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Why do Internet Devices Remain Vulnerable? A Survey with...

Tamara Bondar, Hala Assal, AbdelRahman Abdou (Carleton University)

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WIP: Savvy: Trustworthy Autonomous Vehicles Architecture

Ali Shoker, Rehana Yasmin, Paulo Esteves-Verissimo (Resilient Computing & Cybersecurity Center (RC3), KAUST)

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Folk Models of Misinformation on Social Media

Filipo Sharevski (DePaul University), Amy Devine (DePaul University), Emma Pieroni (DePaul University), Peter Jachim (DePaul University)

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Privacy Starts with UI: Privacy Patterns and Designer Perspectives in UI/UX Practice

Anxhela Maloku (Technical University of Munich), Alexandra Klymenko (Technical University of Munich), Stephen Meisenbacher (Technical University of Munich), Florian Matthes (Technical University of Munich)

Vision: Profiling Human Attackers: Personality and Behavioral Patterns in Deceptive Multi-Stage CTF Challenges

Khalid Alasiri (School of Computing and Augmented Intelligence Arizona State University), Rakibul Hasan (School of Computing and Augmented Intelligence Arizona State University)

From Underground to Mainstream Marketplaces: Measuring AI-Enabled NSFW Deepfakes on Fiverr

Mohamed Moustafa Dawoud (University of California, Santa Cruz), Alejandro Cuevas (Princeton University), Ram Sundara Raman (University of California, Santa Cruz)