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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MyTEE: Own the Trusted Execution Environment on Embedded Devices

Seungkyun Han (Chungnam National University), Jinsoo Jang (Chungnam National University)

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Adversarial Robustness for Tabular Data through Cost and Utility...

Klim Kireev (EPFL), Bogdan Kulynych (EPFL), Carmela Troncoso (EPFL)

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User Attitudes Towards Controls for Ad Interests Estimated On-device...

Florian Lachner, Minzhe Yuan Chen Cheng, Theodore Olsauskas-Warren (Google)

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Bridging the Privacy Gap: Enhanced User Consent Mechanisms on...

Carl Magnus Bruhner (Linkoping University), David Hasselquist (Linkoping University, Sectra Communications), Niklas Carlsson (Linkoping University)

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