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

Online behavioral advertising is a double-edged sword. While relevant display ads are generally considered useful, opaque tracking based on third-party cookies has reached unfettered sprawl and is deemed to be privacy-intrusive. However, existing ways to preserve privacy do not sufficiently balance the needs of both users and the ecosystem. In this work, we evaluate alternative browser controls. We leverage the idea of inferring interests on users’ devices and designed novel browser controls to manage these interests. Through a mixed method approach, we studied how users feel about this approach. First, we conducted pilot interviews with 9 participants to test two design directions. Second, we ran a survey with 2,552 respondents to measure how our final design compares with current cookie settings. Respondents reported a significantly higher level of perceived privacy and feeling of control when introduced to the concept of locally inferred interests with an option for removal.

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Enhanced Vehicular Roll-Jam Attack using a Known Noise Source

Zachary Depp, Halit Bugra Tulay, C. Emre Koksal (The Ohio State University)

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Backdoor Attacks Against Dataset Distillation

Yugeng Liu (CISPA Helmholtz Center for Information Security), Zheng Li (CISPA Helmholtz Center for Information Security), Michael Backes (CISPA Helmholtz Center for Information Security), Yun Shen (Netapp), Yang Zhang (CISPA Helmholtz Center for Information Security)

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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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