Muhammad Ahmad Bashir (Northeastern University), Umar Farooq (LUMS Pakistan), Maryam Shahid (LUMS Pakistan), Muhammad Fareed Zaffar (LUMS Pakistan), Christo Wilson (Northeastern University)

Widely reported privacy issues concerning major online advertising platforms (e.g., Facebook) have heightened concerns among users about the data that is collected about them. However, while we have a comprehensive understanding who collects data on users, as well as how tracking is implemented, there is still a significant gap in our understanding: what information do advertisers actually infer about users, and is this information accurate?

In this study, we leverage Ad Preference Managers (APMs) as a lens through which to address this gap. APMs are transparency tools offered by some advertising platforms that allow users to see the interest profiles that are constructed about them. We recruited 220 participants to install an IRB approved browser extension that collected their interest profiles from four APMs (Google, Facebook, Oracle BlueKai, and Neilsen eXelate), as well as behavioral and survey data. We use this data to analyze the size and correctness of interest profiles, compare their composition across the four platforms, and investigate the origins of the data underlying these profiles.

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Cleaning Up the Internet of Evil Things: Real-World Evidence...

Orcun Cetin (Delft University of Technology), Carlos Gañán (Delft University of Technology), Lisette Altena (Delft University of Technology), Takahiro Kasama (National Institute of Information and Communications Technology), Daisuke Inoue (National Institute of Information and Communications Technology), Kazuki Tamiya (Yokohama National University), Ying Tie (Yokohama National University), Katsunari Yoshioka (Yokohama National University), Michel van Eeten (Delft…

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Countering Malicious Processes with Process-DNS Association

Suphannee Sivakorn (Columbia University), Kangkook Jee (NEC Labs America), Yixin Sun (Princeton University), Lauri Korts-Pärn (Cyber Defense Institute), Zhichun Li (NEC Labs America), Cristian Lumezanu (NEC Labs America), Zhenyu Wu (NEC Labs America), Lu-An Tang (NEC Labs America), Ding Li (NEC Labs America)

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One Engine To Serve 'em All: Inferring Taint Rules...

Zheng Leong Chua (National University of Singapore), Yanhao Wang (TCA/SKLCS, Institute of Software, Chinese Academy of Sciences), Teodora Baluta (National University of Singapore), Prateek Saxena (National University of Singapore), Zhenkai Liang (National University of Singapore), Purui Su (TCA/SKLCS, Institute of Software, Chinese Academy of Sciences)

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Stealthy Adversarial Perturbations Against Real-Time Video Classification Systems

Shasha Li (University of California Riverside), Ajaya Neupane (University of California Riverside), Sujoy Paul (University of California Riverside), Chengyu Song (University of California Riverside), Srikanth V. Krishnamurthy (University of California Riverside), Amit K. Roy Chowdhury (University of California Riverside), Ananthram Swami (United States Army Research Laboratory)

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