A privacy-preserving multimedia recommendation in the context of social network based on weighted noise injection

With the popularity of social networks such as Facebook and Twitter, more information such as individual's social connections is considered to make personalized multimedia recommendation, compared to traditional approaches based on the rating matrix. However, the massive data information used for recommendation often contains much personal privacy information. Once the information is obtained by attackers, user's privacy will be revealed directly or indirectly. This paper proposes a privacy preserving method based on weighted noise injection technique to address the issue of multimedia recommendation in the context of social networks. More specifically, first, we extract core users from entire users. The extracted core users can represent the features of all users adequately. Only the relevant data of core users are then used for rating prediction. Second, we inject different noises to the rating matrix of core users according to different relations between the target user and core users. Third, we use the perturbed matrix to predict the ratings of unused multimedia resources for the target user based on a mixed collaborative filtering approach. By comparing with the traditional noise injection method, the experimental results show that the proposed approach can get better performance of privacy preserving multimedia recommendation.