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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UsersFirst in Practice: Evaluating a User-Centric Threat Modeling Taxonomy...

Alexandra Xinran Li (Carnegie Mellon University), Tian Wang (University of Illinois Urbana-Champaign), Yu-Ju Yang (University of Illinois Urbana-Champaign), Miguel Rivera-Lanas (Carnegie Mellon University), Debeshi Ghosh (Carnegie Mellon University), Hana Habib (Carnegie Mellon University), Lorrie Cranor (Carnegie Mellon University), Norman Sadeh (Carnegie Mellon University)

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“So I Sold My Soul“: Effects of Dark Patterns...

Oksana Kulyk (ITU Copenhagen), Willard Rafnsson (IT University of Copenhagen), Ida Marie Borberg, Rene Hougard Pedersen

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Vision: Profiling Human Attackers: Personality and Behavioral Patterns in...

Khalid Alasiri (School of Computing and Augmented Intelligence Arizona State University), Rakibul Hasan (School of Computing and Augmented Intelligence Arizona State University)

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