  With the rapid growth in multimedia services and the enormous offers of video
contents in online social networks, users have difficulty in obtaining their
interests. Therefore, various personalized recommendation systems have been
proposed. However, they ignore that the accelerated proliferation of social
media data has led to the big data era, which has greatly impeded the process
of video recommendation. In addition, none of them has considered both the
privacy of users' contexts (e,g., social status, ages and hobbies) and video
service vendors' repositories, which are extremely sensitive and of significant
commercial value. To handle the problems, we propose a cloud-assisted
differentially private video recommendation system based on distributed online
learning. In our framework, service vendors are modeled as distributed
cooperative learners, recommending videos according to user's context, while
simultaneously adapting the video-selection strategy based on user-click
feedback to maximize total user clicks (reward). Considering the sparsity and
heterogeneity of big social media data, we also propose a novel geometric
differentially private model, which can greatly reduce the performance
(recommendation accuracy) loss. Our simulation shows the proposed algorithms
outperform other existing methods and keep a delicate balance between computing
accuracy and privacy preserving level.
