HARPO: Learning to Subvert Online Behavioral Advertising

TL;DR

Harpo is a reinforcement learning-based system that intelligently obfuscates user browsing profiles with fake visits, significantly improving privacy and stealthiness against behavioral ad tracking.

Contribution

Harpo introduces a novel learning-based method to effectively subvert online behavioral advertising through adaptive obfuscation, outperforming existing tools.

Findings

Triggers over 40% incorrect interest segments

Achieves 6x higher bid values for user privacy

Outperforms existing obfuscation tools by up to 16x

Abstract

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