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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BARS: Local Robustness Certification for Deep Learning based Traffic...

Kai Wang (Tsinghua University), Zhiliang Wang (Tsinghua University), Dongqi Han (Tsinghua University), Wenqi Chen (Tsinghua University), Jiahai Yang (Tsinghua University), Xingang Shi (Tsinghua University), Xia Yin (Tsinghua University)

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“I found the text to be encouraging” – Evaluating...

Rozalina Doneva (Karlsruhe Institute of Technology (KIT)), Anne Hennig (Karlsruhe Institute of Technology (KIT)), Peter Mayer (University of Southern Denmark (SDU))

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Your Router is My Prober: Measuring IPv6 Networks via...

Long Pan (Tsinghua University), Jiahai Yang (Tsinghua University), Lin He (Tsinghua University), Zhiliang Wang (Tsinghua University), Leyao Nie (Tsinghua University), Guanglei Song (Tsinghua University), Yaozhong Liu (Tsinghua University)

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