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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Sometimes, You Aren’t What You Do: Mimicry Attacks against...

Akul Goyal (University of Illinois at Urbana-Champaign), Xueyuan Han (Wake Forest University), Gang Wang (University of Illinois at Urbana-Champaign), Adam Bates (University of Illinois at Urbana-Champaign)

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The “Beatrix” Resurrections: Robust Backdoor Detection via Gram Matrices

Wanlun Ma (Swinburne University of Technology), Derui Wang (CSIRO’s Data61), Ruoxi Sun (The University of Adelaide & CSIRO's Data61), Minhui Xue (CSIRO's Data61), Sheng Wen (Swinburne University of Technology), Yang Xiang (Digital Research & Innovation Capability Platform, Swinburne University of Technology)

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AutoWatch: Learning Driver Behavior with Graphs for Auto Theft...

Paul Agbaje, Abraham Mookhoek, Afia Anjum, Arkajyoti Mitra (University of Texas at Arlington), Mert D. Pesé (Clemson University), Habeeb Olufowobi (University of Texas at Arlington)

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Do Privacy Labels Answer Users' Privacy Questions?

Shikun Zhang, Norman Sadeh (Carnegie Mellon University)

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