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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Daniel Timko (California State University San Marcos), Daniel Hernandez Castillo (California State University San Marcos), Muhammad Lutfor Rahman (California State University San Marcos)

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Sakuna Harinda Jayasundara, Nalin Asanka Gamagedara Arachchilage, Giovanni Russello (University of Auckland)

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Derrick McKee (Purdue University), Nathan Burow (MIT Lincoln Laboratory), Mathias Payer (EPFL)

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Fine-Grained Trackability in Protocol Executions

Ksenia Budykho (Surrey Centre for Cyber Security, University of Surrey, UK), Ioana Boureanu (Surrey Centre for Cyber Security, University of Surrey, UK), Steve Wesemeyer (Surrey Centre for Cyber Security, University of Surrey, UK), Daniel Romero (NCC Group), Matt Lewis (NCC Group), Yogaratnam Rahulan (5G/6G Innovation Centre - 5GIC/6GIC, University of Surrey, UK), Fortunat Rajaona (Surrey…

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