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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Navigating Murky Waters: Automated Browser Feature Testing for Uncovering...

Mir Masood Ali (University of Illinois Chicago), Binoy Chitale (Stony Brook University), Mohammad Ghasemisharif (University of Illinois Chicago), Chris Kanich (University of Illinois Chicago), Nick Nikiforakis (Stony Brook University), Jason Polakis (University of Illinois Chicago)

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Faster Secure Comparisons with Offline Phase for Efficient Private...

Florian Kerschbaum (University of Waterloo), Erik-Oliver Blass (Airbus), Rasoul Akhavan Mahdavi (University of Waterloo)

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The Walls Have Ears: Gauging Security Awareness in a...

Gokul Jayakrishnan, Vijayanand Banahatti, Sachin Lodha (TCS Research Tata Consultancy Services Ltd.)

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Towards Automatic and Precise Heap Layout Manipulation for General-Purpose...

Runhao Li (National University of Defense Technology), Bin Zhang (National University of Defense Technology), Jiongyi Chen (National University of Defense Technology), Wenfeng Lin (National University of Defense Technology), Chao Feng (National University of Defense Technology), Chaojing Tang (National University of Defense Technology)

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