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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The Advantages of Distributed TCAM Firewalls in Automotive Real-Time...

Evan Allen (Virginia Tech), Zeb Bowden (Virginia Tech Transportation Institute), J. Scot Ransbottom (Virginia Tech)

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Do Not Give a Dog Bread Every Time He...

Chongqing Lei (Southeast University), Zhen Ling (Southeast University), Yue Zhang (Jinan University), Kai Dong (Southeast University), Kaizheng Liu (Southeast University), Junzhou Luo (Southeast University), Xinwen Fu (University of Massachusetts Lowell)

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Semi-Automated Synthesis of Driving Rules

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

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DARWIN: Survival of the Fittest Fuzzing Mutators

Patrick Jauernig (Technical University of Darmstadt), Domagoj Jakobovic (University of Zagreb, Croatia), Stjepan Picek (Radboud University and TU Delft), Emmanuel Stapf (Technical University of Darmstadt), Ahmad-Reza Sadeghi (Technical University of Darmstadt)

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