Differential Privacy Oriented Distributed Online Learning for Mobile Social Video Prefetching

The ever fast growing mobile social video traffic has motivated the urgent requirement of alleviating backbone pressures while ensuring the user-quality experience. Mobile video prefetching previously caches the future accessed videos at the edge, which has become a promising solution for traffic offloading and delay reduction. However, providing high performance prefetching still remains problematic in the presence of high dynamic mobile users' viewing behaviors and consecutive generated video content. Besides, given the fact that making prefetching decision requires viewing history that is sensitive, the increasing privacy issues should also be considered. In this paper, we propose a differential privacy oriented distributed online learning method for mobile social video prefetching (DPDL-SVP). Through a large-scale data analysis based on one of the most popular online social network sites, WeiBo.cn, we reveal that users' viewing behaviors have strong a relation with video preference, content popularity, and social interactions. We then formulate the prefetching problem as an online convex optimization based on these three factors. Furthermore, the problem is divided into two subproblems, and we implement a distributed algorithm separately to solve them with differential privacy. The performance bound of the proposed online algorithms is also theoretically proved. We conduct a series simulation based on real viewing traces to evaluate the performance of DPDL-SVP. Evaluation results show how our proposed algorithms achieve superior performance in terms of the prediction accuracy, delay reduction, and scalability.