Pre-Fetch or not

Xianzhi Zhang, Yipeng Zhou, Linchang Xiao, Di Wu, Miao Hu, John C. S. Lui, Liangbin Zhao

Abstract: As users request their preferred metaversal media, e.g., 360-degree video, user requests tracked by metaverse content providers (MCPs) pose significant privacy leakage risks. Unfortunately, existing privacy-enhancing techniques are largely ineffective in protecting user privacy for metaversal content requests since these requests cannot be easily altered or concealed by users and must remain visible to MCPs to ensure accurate content delivery. To safeguard user privacy in metaverse multimedia services (MMS), one practical approach is pre-fetching multimedia content (e.g., short videos, video patches in 360° videos) that is not directly related to users' interests, thereby preventing MCPs from accurately inferring user preferences. However, plain pre-fetching strategies encounter a critical trade-off between privacy protection and edge caching performance given that MCPs often rely on edges for distributing metaverse content. In this paper, we propose a cache-friendly and privacy-aware content pre-fetching (CRACE) algorithm for user devices (UDs) along with a complementary caching algorithm for edge caches (ECs). CRACE effectively mitigates privacy leakage in metaverse content requests while minimally impacting caching performance. Specifically, we introduce a novel privacy model to guide pre-fetching decisions and formulate a Stackelberg game to analyze strategic interactions between UDs and ECs. We derive optimal strategies that maximize their respective utilities and demonstrate the existence and uniqueness of the Stackelberg equilibrium. Extensive experiments conducted with real-world data demonstrate that CRACE significantly enhances privacy protection, reducing privacy disclosure by up to 59.03% compared to baseline algorithms, with negligible impact on the edge caching performance.

External IDs:doi:10.1109/tsc.2025.3608575