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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Machine Unlearning of Features and Labels

Alexander Warnecke (TU Braunschweig), Lukas Pirch (TU Braunschweig), Christian Wressnegger (Karlsruhe Institute of Technology (KIT)), Konrad Rieck (TU Braunschweig)

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From Paranoia to Compliance: The Bumpy Road of System...

Niklas Busch (CISPA Helmholtz Center for Information Security, Germany), Philip Klostermeyer (CISPA Helmholtz Center for Information Security, Germany), Jan H. Klemmer (CISPA Helmholtz Center for Information Security, Germany), Yasemin Acar (Paderborn University, Germany), Sascha Fahl (CISPA Helmholtz Center for Information Security, Germany)

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Does This App Respect My Privacy? Design and Evaluation...

Oksana Kulyk (Karlsruhe Institute of Technology); Paul Gerber, Karola Marky, Christopher Beckmann (Technische Universität Darmstadt); Melanie Volkamer (Karlsruhe Institute of Technology)

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