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

Securing Federated Sensitive Topic Classification against Poisoning Attacks

Tianyue Chu (IMDEA Networks Institute), Alvaro Garcia-Recuero (IMDEA Networks Institute), Costas Iordanou (Cyprus University of Technology), Georgios Smaragdakis (TU Delft), Nikolaos Laoutaris (IMDEA Networks Institute)

Read More

Applying Accessibility Metrics to Measure the Threat Landscape for...

John Breton, AbdelRahman Abdou (Carleton University)

Read More

Sometimes, You Aren’t What You Do: Mimicry Attacks against...

Akul Goyal (University of Illinois at Urbana-Champaign), Xueyuan Han (Wake Forest University), Gang Wang (University of Illinois at Urbana-Champaign), Adam Bates (University of Illinois at Urbana-Champaign)

Read More

A Robust Counting Sketch for Data Plane Intrusion Detection

Sian Kim (Ewha Womans University), Changhun Jung (Ewha Womans University), RhongHo Jang (Wayne State University), David Mohaisen (University of Central Florida), DaeHun Nyang (Ewha Womans University)

Read More