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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Un-Rocking Drones: Foundations of Acoustic Injection Attacks and Recovery...

Jinseob Jeong (KAIST, Agency for Defense Development), Dongkwan Kim (Samsung SDS), Joonha Jang (KAIST), Juhwan Noh (KAIST), Changhun Song (KAIST), Yongdae Kim (KAIST)

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WIP: Threat Modeling Laser-Induced Acoustic Interference in Computer Vision-Assisted...

Nina Shamsi (Northeastern University), Kaeshav Chandrasekar, Yan Long, Christopher Limbach (University of Michigan), Keith Rebello (Boeing), Kevin Fu (Northeastern 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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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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