Backhaul Traffic-Aware Edge Caching for Recommended Content With Personalized Privacy

Caching recommended contents at the network edge can effectively alleviate the traffic pressure of the backbone network and significantly improve user experience. However, highly personalized and precise recommendations often rely on leveraging more user request records, raising serious privacy concerns. Existing recommendation-aware edge caching mechanisms typically apply a fixed level of privacy protection, without considering the personalized privacy of users. This one-size-fits-all approach often introduces significant noise, adversely impacting cache hit ratio (CHR). In this work, we propose a differential privacy-based edge caching framework supporting personalized privacy-preserving to address these challenges. We formulate a CHR maximization problem under personalized privacy constraints and reveal the NP-completeness of the problem with a rigorous mathematical proof. Subsequently, we mathematically model the relationship between personalized privacy and user preference distortion, analyzing its impact on recommendations and user requests. To solve it, we introduce an efficient heuristic algorithm named the Backhaul Traffic-Aware Caching Algorithm. This algorithm utilizes backhaul traffic as a feedback signal to make accurate caching decisions, enabling adaptive optimization of caching decisions by perceiving the impact of noise and low-quality recommendations. Extensive experiments on two typical real-world datasets validate the effectiveness of our framework, demonstrating its ability to enhance privacy protection while simultaneously improving CHR.