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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Evasion Attacks and Defenses on Smart Home Physical Event...

Muslum Ozgur Ozmen (Purdue University), Ruoyu Song (Purdue University), Habiba Farrukh (Purdue University), Z. Berkay Celik (Purdue University)

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Death By A Thousand COTS: Disrupting Satellite Communications using...

Frederick Rawlins, Richard Baker and Ivan Martinovic (University of Oxford) Presenter: Frederick Rawlins

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Efficient Dynamic Proof of Retrievability for Cold Storage

Tung Le (Virginia Tech), Pengzhi Huang (Cornell University), Attila A. Yavuz (University of South Florida), Elaine Shi (CMU), Thang Hoang (Virginia Tech)

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Adventures in Wonderland: Automotive Cyber beyond the CAN Bus

Michael Westra (In-Vehicle Cyber Security Technical Manager, Ford)

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