A Utility-Preserving Obfuscation Approach for YouTube Recommendations

TL;DR

This paper introduces De-Harpo, a novel obfuscation method for YouTube recommendations that effectively balances user privacy with recommendation utility by combining privacy-preserving obfuscation and denoising techniques.

Contribution

De-Harpo is the first approach to jointly obfuscate user watch history and denoise recommendations, significantly improving utility while maintaining privacy without platform cooperation.

Findings

De-Harpo doubles utility preservation compared to state-of-the-art methods.

It maintains high privacy levels and robustness against de-obfuscation attacks.

The approach is scalable and effective in large-scale evaluations.

Abstract

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.