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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VICEROY: GDPR-/CCPA-compliant Enforcement of Verifiable Accountless Consumer Requests

Scott Jordan (University of California, Irvine), Yoshimichi Nakatsuka (University of California, Irvine), Ercan Ozturk (University of California, Irvine), Andrew Paverd (Microsoft Research), Gene Tsudik (University of California, Irvine)

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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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WIP: Augmenting Vehicle Safety With Passive BLE

Noah T. Curran (University of Michigan), Kang G. Shin (University of Michigan), William Hass (Lear Corporation), Lars Wolleschensky (Lear Corporation), Rekha Singoria (Lear Corporation), Isaac Snellgrove (Lear Corporation), Ran Tao (Lear Corporation)

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An Exploratory study of Malicious Link Posting on Social...

Muhammad Hassan, Mahnoor Jameel, Masooda Bashir (University of Illinois at Urbana Champaign)

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