Diego Ortiz, Leilani Gilpin, Alvaro A. Cardenas (University of California, Santa Cruz)

Autonomous vehicles must operate in a complex environment with various social norms and expectations. While most of the work on securing autonomous vehicles has focused on safety, we argue that we also need to monitor for deviations from various societal “common sense” rules to identify attacks against autonomous systems. In this paper, we provide a first approach to encoding and understanding these common-sense driving behaviors by semi-automatically extracting rules from driving manuals. We encode our driving rules in a formal specification and make our rules available online for other researchers.

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Power to the Data Defenders: Human-Centered Disclosure Risk Calibration...

Kaustav Bhattacharjee, Aritra Dasgupta (New Jersey Institute of Technology)

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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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SoundLock: A Novel User Authentication Scheme for VR Devices...

Huadi Zhu (The University of Texas at Arlington), Mingyan Xiao (The University of Texas at Arlington), Demoria Sherman (The University of Texas at Arlington), Ming Li (The University of Texas at Arlington)

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Privacy-Preserving Database Fingerprinting

Tianxi Ji (Texas Tech University), Erman Ayday (Case Western Reserve University), Emre Yilmaz (University of Houston-Downtown), Ming Li (CSE Department The University of Texas at Arlington), Pan Li (Case Western Reserve University)

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