Toward Personalized Privacy-Preserving Content Caching With Edge Cooperation

Caching content at the edge network has emerged as a critical technique to alleviate backhaul congestion, minimize service latency, and improve user Quality of Experience (QoE). Driven by operational profitability, the edge service provider (ESP) necessitates access to user preference data to optimize its caching policies. However, disseminating such sensitive information raises significant privacy concerns. To address this challenge, we propose a privacy-preserving cooperative edge caching framework that jointly enhances caching efficiency and safeguards user preference privacy. Specifically, we design a privacy-oriented popularity estimation protocol, PSRSA, which guarantees the preservation of user privacy while delivering precise content popularity estimations. The PSRSA protocol integrates a randomize-then-shuffle mechanism to obfuscate user data locally prior to aggregation and accommodates heterogeneous privacy preservation requirements across users by dynamically adjusting differential privacy (DP) budgets. Subsequently, we formulate a Stackelberg Game-based Edge Caching (SGEC) algorithm that jointly optimizes the utility functions of both the content provider (CP) and the ESP, thereby ensuring efficient resource allocation at edge servers. Comprehensive evaluations conducted on real-world datasets demonstrate the superiority of the integrated PSRSA+SGEC framework, exhibiting a 35.22% increase in caching utility and a 3.4% improvement in cache hit ratio, while simultaneously achieving a 20.1% reduction in privacy leakage risks, compared to state-of-the-art baselines.