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: Practical Removal Attacks on LiDAR-based Object Detection in...

Takami Sato (University of California, Irvine), Yuki Hayakawa (Keio University), Ryo Suzuki (Keio University), Yohsuke Shiiki (Keio University), Kentaro Yoshioka (Keio University), Qi Alfred Chen (University of California, Irvine)

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MyTEE: Own the Trusted Execution Environment on Embedded Devices

Seungkyun Han (Chungnam National University), Jinsoo Jang (Chungnam National University)

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Extrapolating Formal Analysis to Uncover Attacks in Bluetooth Passkey...

Mohit Kumar Jangid (The Ohio State University), Yue Zhang (Computer Science & Engineering, Ohio State University), Zhiqiang Lin (The Ohio State University)

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Automatic Retrieval of Privacy Factors from IoMT Policies: ML...

Nyteisha Bookert, Mohd Anwar (North Carolina Agricultural and Technical State University)

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