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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REaaS: Enabling Adversarially Robust Downstream Classifiers via Robust Encoder...

Wenjie Qu (Huazhong University of Science and Technology), Jinyuan Jia (University of Illinois Urbana-Champaign), Neil Zhenqiang Gong (Duke University)

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No Grammar, No Problem: Towards Fuzzing the Linux Kernel...

Alexander Bulekov (Boston University), Bandan Das (Red Hat), Stefan Hajnoczi (Red Hat), Manuel Egele (Boston University)

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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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WIP: Towards the Practicality of the Adversarial Attack on...

Chen Ma (Xi'an Jiaotong University), Ningfei Wang (University of California, Irvine), Qi Alfred Chen (University of California, Irvine), Chao Shen (Xi'an Jiaotong University)

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