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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Frederick Rawlins, Richard Baker and Ivan Martinovic (University of Oxford) Presenter: Frederick Rawlins

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Yuri Gbur (Technische Universität Berlin), Florian Tschorsch (Technische Universität Berlin)

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Hugo Lefeuvre (The University of Manchester), Vlad-Andrei Bădoiu (University Politehnica of Bucharest), Yi Chen (Rice University), Felipe Huici (Unikraft.io), Nathan Dautenhahn (Rice University), Pierre Olivier (The University of Manchester)

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Chen Ma (Xi'an Jiaotong University), Ningfei Wang (University of California, Irvine), Qi Alfred Chen (University of California, Irvine), Chao Shen (Xi'an Jiaotong University)

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