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.

View More Papers

Lightning Community Shout-Outs to:

(1) Jonathan Petit, Secure ML Performance Benchmark (Qualcomm) (2) David Balenson, The Road to Future Automotive Research Datasets: PIVOT Project and Community Workshop (USC Information Sciences Institute) (3) Jeremy Daily, CyberX Challenge Events (Colorado State University) (4) Mert D. Pesé, DETROIT: Data Collection, Translation and Sharing for Rapid Vehicular App Development (Clemson University) (5) Ning…

Read More

Un-Rocking Drones: Foundations of Acoustic Injection Attacks and Recovery...

Jinseob Jeong (KAIST, Agency for Defense Development), Dongkwan Kim (Samsung SDS), Joonha Jang (KAIST), Juhwan Noh (KAIST), Changhun Song (KAIST), Yongdae Kim (KAIST)

Read More

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)

Read More

Learning Automated Defense Strategies Using Graph-Based Cyber Attack Simulations

Jakob Nyber, Pontus Johnson (KTH Royal Institute of Technology)

Read More