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

HeteroScore: Evaluating and Mitigating Cloud Security Threats Brought by...

Chongzhou Fang (University of California, Davis), Najmeh Nazari (University of California, Davis), Behnam Omidi (George Mason University), Han Wang (Temple University), Aditya Puri (Foothill High School, Pleasanton, CA), Manish Arora (LearnDesk, Inc.), Setareh Rafatirad (University of California, Davis), Houman Homayoun (University of California, Davis), Khaled N. Khasawneh (George Mason University)

Read More

Backdoor Attacks Against Dataset Distillation

Yugeng Liu (CISPA Helmholtz Center for Information Security), Zheng Li (CISPA Helmholtz Center for Information Security), Michael Backes (CISPA Helmholtz Center for Information Security), Yun Shen (Netapp), Yang Zhang (CISPA Helmholtz Center for Information Security)

Read More

ChargePrint: A Framework for Internet-Scale Discovery and Security Analysis...

Tony Nasr (Concordia University), Sadegh Torabi (George Mason University), Elias Bou-Harb (University of Texas at San Antonio), Claude Fachkha (University of Dubai), Chadi Assi (Concordia University)

Read More

Why do Internet Devices Remain Vulnerable? A Survey with...

Tamara Bondar, Hala Assal, AbdelRahman Abdou (Carleton University)

Read More