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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Detection and Resolution of Control Decision Anomalies

Prof. Kang Shin (Kevin and Nancy O'Connor Professor of Computer Science, and the Founding Director of the Real-Time Computing Laboratory (RTCL) in the Electrical Engineering and Computer Science Department at the University of Michigan)

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Towards Integrating Human-Centered Cybersecurity Research Into Practice: A Practitioner...

Julie Haney, Clyburn Cunningham, Susanne Furman (National Institute of Standards and Technology)

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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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Evaluating Personal Data Control In Mobile Applications Using Heuristics

Alain Giboin (UCA, INRIA, CNRS, I3S), Karima Boudaoud (UCA, CNRS, I3S), Patrice Pena (Userthink), Yoann Bertrand (UCA, CNRS, I3S), Fabien Gandon (UCA, INRIA, CNRS, I3S)

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