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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ProbFlow : Using Probabilistic Programming in Anonymous Communication Networks

Hussein Darir (University of Illinois Urbana-Champaign), Geir Dullerud (University of Illinois Urbana-Champaign), Nikita Borisov (University of Illinois Urbana-Champaign)

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WIP: AMICA: Attention-based Multi-Identifier model for asynchronous intrusion detection...

Natasha Alkhatib (Télécom Paris), Lina Achaji (INRIA), Maria Mushtaq (Télécom Paris), Hadi Ghauch (Télécom Paris), Jean-Luc Danger (Télécom Paris)

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Non-Interactive Privacy-Preserving Sybil-Free Authentication Scheme in VANETs

Mahdi Akil (Karlstad University), Leonardo Martucci (Karlstad University), Jaap-Henk Hoepman (Radboud University)

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VASP: V2X Application Spoofing Platform

Mohammad Raashid Ansari, Jonathan Petit, Jean-Philippe Monteuuis, Cong Chen (Qualcomm Technologies, Inc.)

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