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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Michael Westra (In-Vehicle Cyber Security Technical Manager, Ford)

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Callie Monroe, Faiza Tazi, Sanchari Das (university of Denver)

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How to Count Bots in Longitudinal Datasets of IP...

Leon Böck (Technische Universität Darmstadt), Dave Levin (University of Maryland), Ramakrishna Padmanabhan (CAIDA), Christian Doerr (Hasso Plattner Institute), Max Mühlhäuser (Technical University of Darmstadt)

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Modeling End-User Affective Discomfort With Mobile App Permissions Across...

Yuxi Wu (Georgia Institute of Technology and Northeastern University), Jacob Logas (Georgia Institute of Technology), Devansh Ponda (Georgia Institute of Technology), Julia Haines (Google), Jiaming Li (Google), Jeffrey Nichols (Apple), W. Keith Edwards (Georgia Institute of Technology), Sauvik Das (Carnegie Mellon University)

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