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: Modeling and Detecting Falsified Vehicle Trajectories Under Data...

Jun Ying, Yiheng Feng (Purdue University), Qi Alfred Chen (University of California, Irvine), Z. Morley Mao (University of Michigan and Google)

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Short: Rethinking Secure Pairing in Drone Swarms

Muslum Ozgur Ozmen, Habiba Farrukh, Hyungsub Kim, Antonio Bianchi, Z. Berkay Celik (Purdue University)

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WIP: Adversarial Object-Evasion Attack Detection in Autonomous Driving Contexts:...

Rao Li (The Pennsylvania State University), Shih-Chieh Dai (Pennsylvania State University), Aiping Xiong (Penn State University)

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