Recommender systems have become an integral part of many social networks and extract knowledge from a user’s personal and sensitive data both explicitly, with the user’s knowledge, and implicitly. This trend has created major privacy concerns as users are mostly unaware of what data and how much data is being used and how securely it is used.
In order to increase the data utility in such social networks, recommender systems can deliver personalization of a collection of items to online social network users based on their nature. Meanwhile, the personalized suggestions and recommendations in these systems are heavily dependent on users’ information. This can increase the probability of information leakage of users in such networks. Moreover, information sharing creates real threats to a user’s privacy. In this case, there is a need for data protection. Fundamentally, data protection means clear sets of rules and regulations, policies and diverse measures that provided for information security and lessening the invasion into a user’s privacy.
Privacy in social network sites can be seen from two different perspectives. The first perspective is local privacy or user-centric, which is known as social privacy. The second perspective is global or network-centric which is known as institutional privacy. From the user-centric perspective, users decide what to share with others while they can create various levels and circles of friends, posts and information to whom they intend to share. From the global view, social network sites take advantage of users’ information for different goals as stated and detailed in data usage rule and policy. Moreover, the network-centric privacy can also be seen from two distinctive approaches.
Although providing a suitable level of privacy for every mentioned perspective is burdensome, training users and increase their knowledge about data privacy could help to preserve the information of many individuals who have online presence on online social networks.