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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RR: A Fault Model for Efficient TEE Replication

Baltasar Dinis (Instituto Superior Técnico (IST-ULisboa) / INESC-ID / MPI-SWS), Peter Druschel (MPI-SWS), Rodrigo Rodrigues (Instituto Superior Técnico (IST-ULisboa) / INESC-ID)

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Security Awareness Training through Experiencing the Adversarial Mindset

Jens Christian Dalgaard, Niek A. Janssen, Oksana Kulyuk, Carsten Schurmann (IT University of Copenhagen)

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OptRand: Optimistically Responsive Reconfigurable Distributed Randomness

Adithya Bhat (Purdue University), Nibesh Shrestha (Rochester Institute of Technology), Aniket Kate (Purdue University), Kartik Nayak (Duke University)

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RAI2: Responsible Identity Audit Governing the Artificial Intelligence

Tian Dong (Shanghai Jiao Tong University), Shaofeng Li (Shanghai Jiao Tong University), Guoxing Chen (Shanghai Jiao Tong University), Minhui Xue (CSIRO's Data61), Haojin Zhu (Shanghai Jiao Tong University), Zhen Liu (Shanghai Jiao Tong University)

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