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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Why do Internet Devices Remain Vulnerable? A Survey with...

Tamara Bondar, Hala Assal, AbdelRahman Abdou (Carleton University)

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Measuring Messengers: Analyzing Infrastructures and Message Timings to Extract...

Theodor Schnitzler (Research Center Trustworthy Data Science and Security, TU Dortmund, and Ruhr-Universität Bochum)

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HistCAN: A real-time CAN IDS with enhanced historical traffic...

Shuguo Zhuo, Nuo Li, Kui Ren (The State Key Laboratory of Blockchain and Data Security, Zhejiang University)

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Fine-Grained Trackability in Protocol Executions

Ksenia Budykho (Surrey Centre for Cyber Security, University of Surrey, UK), Ioana Boureanu (Surrey Centre for Cyber Security, University of Surrey, UK), Steve Wesemeyer (Surrey Centre for Cyber Security, University of Surrey, UK), Daniel Romero (NCC Group), Matt Lewis (NCC Group), Yogaratnam Rahulan (5G/6G Innovation Centre - 5GIC/6GIC, University of Surrey, UK), Fortunat Rajaona (Surrey…

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