HARPO: Learning to Subvert Online Behavioral Advertising
HARPO: Learning to Subvert Online Behavioral Advertising
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DOI：
10.14722/ndss.2022.23062
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发表时间：
2021-11
期刊：
影响因子：
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通讯作者：
Jiang Zhang;K. Psounis;Muhammad Haroon;Zubair Shafiq
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文献类型：
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作者：
Jiang Zhang;K. Psounis;Muhammad Haroon;Zubair Shafiq
在线行为广告和相关的跟踪设备构成了真正的隐私威胁。不幸的是，现有的隐私增强工具并不总是对在线广告和跟踪有效。我们提出了HARPO，这是一种基于学习的原则性方法，可以通过混淆来颠覆在线行为广告。Harpo使用强化学习自适应地将真实页面访问与虚假页面交织在一起，以扭曲跟踪者对用户浏览配置文件的看法。我们根据用于在线行为广告的真实用户概况和广告定向模型对Harpo进行评估。结果显示，Harpo通过触发超过40%的错误兴趣片段和6倍以上的出价来提高隐私。在相同的开销下，HARPO的性能比现有的模糊处理工具高出16倍。与现有的混淆工具相比，HARPO还能够实现更好的对抗性检测的隐蔽性。Harpo在利用混淆来颠覆在线行为广告方面取得了有意义的进展
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 6x higher bid values. Harpo outperforms existing obfuscation tools by as much as 16x 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