Jiang Zhang (University of Southern California), Konstantinos Psounis (University of Southern California), Muhammad Haroon (University of California, Davis), Zubair Shafiq (University of California, Davis)

Online behavioral advertising, and the associated tracking paraphernalia, poses a real privacy threat. Unfortunately, existing privacy-enhancing tools are not always effective against online advertising and tracking. We propose HARPO, a principled learning-based approach to subvert online behavioral advertising through obfuscation. HARPO uses reinforcement learning to adaptively interleave real page visits with fake pages to distort a tracker’s view of a user’s browsing profile. We evaluate HARPO against real-world user profiling and ad targeting models used for online behavioral advertising. The results show that HARPO improves privacy by triggering more than 40% incorrect interest segments and 6×higher bid values. HARPO outperforms existing obfuscation tools by as much as 16×for the same overhead. HARPO is also able to achieve better stealthiness to adversarial detection than existing obfuscation tools. HARPO meaningfully advances the state-of-the-art in leveraging obfuscation to subvert online behavioral advertising.

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Yuanda Wang (Michigan State University), Hanqing Guo (Michigan State University), Qiben Yan (Michigan State University)

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Jinwoo Kim (KAIST), Eduard Marin (Telefonica Research (Spain)), Mauro Conti (University of Padua), Seungwon Shin (KAIST)

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Edwin Yang (University of Oklahoma) and Song Fang (University of Oklahoma)

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