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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Let Me Unwind That For You: Exceptions to Backward-Edge...

Victor Duta (Vrije Universiteit Amsterdam), Fabian Freyer (University of California San Diego), Fabio Pagani (University of California, Santa Barbara), Marius Muench (Vrije Universiteit Amsterdam), Cristiano Giuffrida (Vrije Universiteit Amsterdam)

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FUZZILLI: Fuzzing for JavaScript JIT Compiler Vulnerabilities

Samuel Groß (Google), Simon Koch (TU Braunschweig), Lukas Bernhard (Ruhr-University Bochum), Thorsten Holz (CISPA Helmholtz Center for Information Security), Martin Johns (TU Braunschweig)

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Adversarial Robustness for Tabular Data through Cost and Utility...

Klim Kireev (EPFL), Bogdan Kulynych (EPFL), Carmela Troncoso (EPFL)

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