一种针对YouTube推荐的效用保持型混淆方法
A Utility-Preserving Obfuscation Approach for YouTube Recommendations
AI总结：
该研究针对现有YouTube推荐混淆方法隐私增强时效用下降的问题，提出带去噪器的De-Harpo混淆方法，在同等隐私水平下效用保留能力达现有最优方法的2倍，兼具隐蔽性与反混淆鲁棒性。
摘要：
在线内容平台通过向用户提供个性化推荐来优化用户参与度。这些推荐系统会追踪并构建用户画像，以预测用户可能感兴趣的相关内容。尽管个性化推荐能为用户提供效用，但支撑其运行的追踪与画像构建行为却带来了隐私问题，因为平台可能会推断出用户潜在的敏感兴趣。目前，人们对构建无需在线内容平台配合的隐私增强混淆方法的兴趣日益浓厚。然而，现有混淆方法主要聚焦于增强隐私，却同时会降低效用，因为混淆操作会引入不相关的推荐内容。我们设计并实现了De-Harpo，这是一种针对YouTube推荐系统的混淆方法，它不仅能混淆用户的视频观看历史以保护隐私，还能对YouTube给出的视频推荐进行去噪，以保留推荐的效用。与以往的混淆方法不同，De-Harpo新增了一个去噪器，该去噪器既利用“秘密”输入（即用户的真实观看历史），也利用对抗性推荐系统同样可获取的信息（即混淆后的观看历史及对应的“含噪”推荐）。我们对De-Harpo开展的大规模评估显示，在相同隐私水平下，其效用保留能力优于现有最优方法2倍，同时还具备隐蔽性，且对反混淆操作具有鲁棒性。
英文摘要：
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.
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