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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dewolf: Improving Decompilation by leveraging User Surveys

Steffen Enders, Eva-Maria C. Behner, Niklas Bergmann, Mariia Rybalka, Elmar Padilla (Fraunhofer FKIE, Germany), Er Xue Hui, Henry Low, Nicholas Sim (DSO National Laboratories, Singapore)

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Ghost Domain Reloaded: Vulnerable Links in Domain Name Delegation...

Xiang Li (Tsinghua University), Baojun Liu (Tsinghua University), Xuesong Bai (University of California, Irvine), Mingming Zhang (Tsinghua University), Qifan Zhang (University of California, Irvine), Zhou Li (University of California, Irvine), Haixin Duan (Tsinghua University; QI-ANXIN Technology Research Institute; Zhongguancun Laboratory), Qi Li (Tsinghua University; Zhongguancun Laboratory)

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CANtropy: Time Series Feature Extraction-Based Intrusion Detection Systems for...

Md Hasan Shahriar, Wenjing Lou, Y. Thomas Hou (Virginia Polytechnic Institute and State University)

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