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With the rapid growth in multimedia services and the enormous offers of videocontents in online social networks, users have difficulty in obtaining theirinterests. Therefore, various personalized recommendation systems have beenproposed. However, they ignore that the accelerated proliferation of socialmedia data has led to the big data era, which has greatly impeded the processof video recommendation. In addition, none of them has considered both theprivacy of users' contexts (e,g., social status, ages and hobbies) and videoservice vendors' repositories, which are extremely sensitive and of significantcommercial value. To handle the problems, we propose a cloud-assisteddifferentially private video recommendation system based on distributed onlinelearning. In our framework, service vendors are modeled as distributedcooperative learners, recommending videos according to user's context, whilesimultaneously adapting the video-selection strategy based on user-clickfeedback to maximize total user clicks (reward). Considering the sparsity andheterogeneity of big social media data, we also propose a novel geometricdifferentially private model, which can greatly reduce the performance(recommendation accuracy) loss. Our simulation shows the proposed algorithmsoutperform other existing methods and keep a delicate balance between computingaccuracy and privacy preserving level.

Differentially Private Online Learning for Cloud-Based Video Recommendation with Multimedia Big Data in Social Networks