  Online content platforms optimize engagement by providing personalized
recommendations to their users. These recommendation systems track and profile
users to predict relevant content a user is likely interested in. While the
personalized recommendations provide utility to users, the tracking and
profiling that enables them poses a privacy issue because the platform might
infer potentially sensitive user interests. There is increasing interest in
building privacy-enhancing obfuscation approaches that do not rely on
cooperation from online content platforms. However, existing obfuscation
approaches primarily focus on enhancing privacy but at the same time they
degrade the utility because obfuscation introduces unrelated recommendations.
We design and implement De-Harpo, an obfuscation approach for YouTube's
recommendation system that not only obfuscates a user's video watch history to
protect privacy but then also denoises the video recommendations by YouTube to
preserve their utility. In contrast to prior obfuscation approaches, De-Harpo
adds a denoiser that makes use of a "secret" input (i.e., a user's actual watch
history) as well as information that is also available to the adversarial
recommendation system (i.e., obfuscated watch history and corresponding "noisy"
recommendations). Our large-scale evaluation of De-Harpo shows that it
outperforms the state-of-the-art by a factor of 2x in terms of preserving
utility for the same level of privacy, while maintaining stealthiness and
robustness to de-obfuscation.
