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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He-HTLC: Revisiting Incentives in HTLC

Sarisht Wadhwa (Duke University), Jannis Stoeter (Duke University), Fan Zhang (Duke University, Yale University), Kartik Nayak (Duke University)

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Do Privacy Labels Answer Users' Privacy Questions?

Shikun Zhang, Norman Sadeh (Carnegie Mellon University)

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Cryptographic Oracle-based Conditional Payments

Varun Madathil (North Carolina State University), Sri Aravinda Krishnan Thyagarajan (NTT Research), Dimitrios Vasilopoulos (IMDEA Software Institute), Lloyd Fournier (None), Giulio Malavolta (Max Planck Institute for Security and Privacy), Pedro Moreno-Sanchez (IMDEA Software Institute)

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An OS-agnostic Approach to Memory Forensics

Andrea Oliveri (EURECOM), Matteo Dell'Amico (University of Genoa), Davide Balzarotti (EURECOM)

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