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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WIP: AMICA: Attention-based Multi-Identifier model for asynchronous intrusion detection...

Natasha Alkhatib (Télécom Paris), Lina Achaji (INRIA), Maria Mushtaq (Télécom Paris), Hadi Ghauch (Télécom Paris), Jean-Luc Danger (Télécom Paris)

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BANS: Evaluation of Bystander Awareness Notification Systems for Productivity...

Shady Mansour (LMU Munich), Pascal Knierim (Universitat Innsbruck), Joseph O’Hagan (University of Glasgow), Florian Alt (University of the Bundeswehr Munich), Florian Mathis (University of Glasgow)

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Faster Secure Comparisons with Offline Phase for Efficient Private...

Florian Kerschbaum (University of Waterloo), Erik-Oliver Blass (Airbus), Rasoul Akhavan Mahdavi (University of Waterloo)

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Augmented Reality’s Potential for Identifying and Mitigating Home Privacy...

Stefany Cruz (Northwestern University), Logan Danek (Northwestern University), Shinan Liu (University of Chicago), Christopher Kraemer (Georgia Institute of Technology), Zixin Wang (Zhejiang University), Nick Feamster (University of Chicago), Danny Yuxing Huang (New York University), Yaxing Yao (University of Maryland), Josiah Hester (Georgia Institute of Technology)

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