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: Adversarial Retroreflective Patches: A Novel Stealthy Attack on...

Go Tsuruoka (Waseda University), Takami Sato, Qi Alfred Chen (University of California, Irvine), Kazuki Nomoto, Ryunosuke Kobayashi, Yuna Tanaka (Waseda University), Tatsuya Mori (Waseda University/NICT/RIKEN)

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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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Firefly: Spoofing Earth Observation Satellite Data through Radio Overshadowing

Edd Salkield, Sebastian Köhler, Simon Birnbach, Richard Baker (University of Oxford). Martin Strohmeier (armasuisse S+T), Ivan Martinovic (University of Oxford) Presenter: Edd Salkield

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Securing Automotive Software Supply Chains (Long)

Marina Moore, Aditya Sirish A Yelgundhalli (New York University), Justin Cappos (NYU)

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