As users conveniently stream their favorite online videos, video request records are automatically stored by video content providers, which have a high chance of privacy leakage. Unfortunately, most existing privacy-enhancing approaches are not applicable for protecting user privacy in video requests, because they 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 that are irrelevant to users' interests so that content providers cannot precisely infer users' interest information. However, a naive redundant requesting approach would significantly degrade the performance of edge caches and increase bandwidth overhead. 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 tackle the problem, we first 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 prove the existence and uniqueness of the equilibrium of the Stackelberg game. Extensive experiments are conducted with real traces to demonstrate that cRVR can effectively protect video request privacy by reducing up to 59.03\% of privacy disclosure compared to baseline algorithms. Meanwhile, the caching performance of EC is only slightly affected.

翻译：随着用户便捷地在线流式播放其喜爱的视频，视频内容提供商自动存储的视频请求记录存在较高的隐私泄露风险。遗憾的是，现有的大多数隐私增强方法并不适用于保护视频请求中的用户隐私，因为这些请求无法被用户轻易修改或扭曲，且必须对内容提供商可见以确保正确视频的流式传输。为保护在线视频服务中的请求隐私，一种可行方案是请求与用户兴趣无关的额外视频，使得内容提供商无法精确推断用户的兴趣信息。然而，简单的冗余请求策略会显著降低边缘缓存性能并增加带宽开销。本文首次提出了一种面向用户设备（UDs）的缓存友好型冗余视频请求（cRVR）算法及其对应的边缘缓存（EC）缓存算法，该方案能以对EC性能的最小影响有效缓解请求隐私泄露问题。为解决该问题，我们首先构建了一个Stackelberg博弈模型来分析UDs与EC之间的交互机制，并通过最大化各自效用来获取其最优策略。对于UDs，其效用函数综合了视频播放效用与隐私保护效用。我们证明了该Stackelberg博弈均衡的存在性与唯一性。基于真实数据轨迹的大量实验表明，相较于基线算法，cRVR能通过降低高达59.03%的隐私泄露量有效保护视频请求隐私，同时仅对EC的缓存性能产生轻微影响。