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

Do Privacy Labels Answer Users' Privacy Questions?

Shikun Zhang, Norman Sadeh (Carnegie Mellon University)

Read More

Security Attacks to the Name Management Protocol in Vehicular...

Sharika Kumar (The Ohio State University), Imtiaz Karim, Elisa Bertino (Purdue University), Anish Arora (Ohio State University)

Read More

Securing EV charging system against Physical-layer Signal Injection Attack...

Soyeon Son (Korea University) Kyungho Joo (Korea University) Wonsuk Choi (Korea University) Dong Hoon Lee (Korea University)

Read More

How to Count Bots in Longitudinal Datasets of IP...

Leon Böck (Technische Universität Darmstadt), Dave Levin (University of Maryland), Ramakrishna Padmanabhan (CAIDA), Christian Doerr (Hasso Plattner Institute), Max Mühlhäuser (Technical University of Darmstadt)

Read More