As users conveniently stream their favored online videos, video request records will be automatically seized by video content providers, which may leak users' privacy. Unfortunately, most existing privacy-enhancing approaches are not applicable for protecting users' privacy in requests, which cannot be easily altered or distorted by users and must be visible for content providers to stream correct videos. To preserve request privacy in online video services, it is possible to request additional videos irrelevant to users' interests so that content providers cannot precisely infer users' interest information. However, a naive redundant requesting approach will significantly degrade the performance of edge caches and increase bandwidth overhead accordingly. In this paper, we are among the first to propose a Cache-Friendly Redundant Video Requesting (cRVR) algorithm for User Devices (UDs) and its corresponding caching algorithm for the Edge Cache (EC), which can effectively mitigate the problem of request privacy leakage with minimal impact on the EC's performance. To solve the problem, we develop a Stackelberg game to analyze the dedicated interaction between UDs and EC and obtain their optimal strategies to maximize their respective utility. For UDs, the utility function is a combination of both video playback utility and privacy protection utility. We theoretically prove the existence and uniqueness of the equilibrium of the Stackelberg game. In the end, extensive experiments are conducted with real traces to demonstrate that cRVR can effectively protect video request privacy by reducing up to 57.96\% of privacy disclosure compared to baseline algorithms. Meanwhile, the caching performance of ECs is only slightly affected.

翻译：随着用户便捷地在线观看喜爱的视频，视频请求记录会被视频内容提供商自动获取，从而可能泄露用户隐私。然而，现有大多数隐私增强方法并不适用于保护用户请求隐私，因为请求内容既不能被用户轻易篡改或扭曲，又必须对内容提供商可见以提供正确的视频流。为保护在线视频服务中的请求隐私，一种可行方案是额外请求与用户兴趣无关的视频，使得内容提供商无法准确推断用户兴趣信息。但简单的冗余请求方法会显著降低边缘缓存性能并增加带宽开销。本文首次提出一种面向用户设备的缓存友好型冗余视频请求算法（cRVR）及其对应的边缘缓存算法，该算法能在最小化影响边缘缓存性能的同时有效缓解请求隐私泄露问题。为解决该问题，我们构建了Stackelberg博弈模型来分析用户设备与边缘缓存之间的特定交互，并获得两者最大化自身效用的最优策略。对于用户设备，其效用函数由视频播放效用和隐私保护效用共同构成。我们从理论上证明了该Stackelberg博弈均衡的存在性与唯一性。最后，基于真实数据集的实验表明，与基线算法相比，cRVR可有效保护视频请求隐私，隐私泄露率最高降低57.96%，同时仅对边缘缓存的缓存性能产生轻微影响。