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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Fusion: Efficient and Secure Inference Resilient to Malicious Servers

Caiqin Dong (Jinan University), Jian Weng (Jinan University), Jia-Nan Liu (Jinan University), Yue Zhang (Jinan University), Yao Tong (Guangzhou Fongwell Data Limited Company), Anjia Yang (Jinan University), Yudan Cheng (Jinan University), Shun Hu (Jinan University)

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On the Feasibility of Profiling Electric Vehicles through Charging...

Ankit Gangwal (IIIT Hyderabad), Aakash Jain (IIIT Hyderabad) and Mauro Conti (University of Padua)

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Efficient Dynamic Proof of Retrievability for Cold Storage

Tung Le (Virginia Tech), Pengzhi Huang (Cornell University), Attila A. Yavuz (University of South Florida), Elaine Shi (CMU), Thang Hoang (Virginia Tech)

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