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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Accountable Javascript Code Delivery

Ilkan Esiyok (CISPA Helmholtz Center for Information Security), Pascal Berrang (University of Birmingham & Nimiq), Katriel Cohn-Gordon (Meta), Robert Künnemann (CISPA Helmholtz Center for Information Security)

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Preventing SIM Box Fraud Using Device Model Fingerprinting

BeomSeok Oh (KAIST), Junho Ahn (KAIST), Sangwook Bae (KAIST), Mincheol Son (KAIST), Yonghwa Lee (KAIST), Min Suk Kang (KAIST), Yongdae Kim (KAIST)

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A Comparison of Three Approaches to Assist Users in...

Michael Clark (Brigham Young University), Scott Ruoti (The University of Tennessee), Michael Mendoza (Imperial College London), Kent Seamons (Brigham Young University)

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