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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Blaze: A Framework for Interprocedural Binary Analysis

Matthew Revelle, Matt Parker, Kevin Orr (Kudu Dynamics)

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Keynote: Cybersecurity Experimentation of the Future

Jelena Mirkovic (USC Information Sciences Institute)

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Operationalizing Cybersecurity Research Ethics Review: From Principles and Guidelines...

Dennis Reidsma, Jeroen van der Ham, and Andrea Continella (University of Twente)

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