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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BlockScope: Detecting and Investigating Propagated Vulnerabilities in Forked Blockchain...

Xiao Yi (The Chinese University of Hong Kong), Yuzhou Fang (The Chinese University of Hong Kong), Daoyuan Wu (The Chinese University of Hong Kong), Lingxiao Jiang (Singapore Management University)

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Improving In-vehicle Networks Intrusion Detection Using On-Device Transfer Learning

Sampath Rajapaksha (Robert Gordon University), Harsha Kalutarage (Robert Gordon University), M.Omar Al-Kadri (Birmingham City University), Andrei Petrovski (Robert Gordon University), Garikayi Madzudzo (Horiba Mira Ltd)

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A Security Study about Electron Applications and a Programming...

Zihao Jin (Microsoft Research and Tsinghua University), Shuo Chen (Microsoft Research), Yang Chen (Microsoft Research), Haixin Duan (Tsinghua University and Quancheng Laboratory), Jianjun Chen (Tsinghua University and Zhongguancun Laboratory), Jianping Wu (Tsinghua University)

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