FriendGuard

Abstract

With the prevalence of online social networking, a large amount of studies have focused on online users' privacy. Existing work has heavily focused on preventing unauthorized access of one's personal information (e.g. locations, posts and photos). Very little research has been devoted into protecting the friend search engine, a service that allows people to explore others' friend lists. Although most friend search engines only disclose a partial view of one's friend list (e.g., k friends) or offer the ability to show all or no friends, attackers may leverage the combined knowledge from views obtained from different queries to gain a much larger social network of a targeted victim, potentially revealing sensitive information of a victim. In this paper, we propose a new friend search engine, namely FriendGuard, which guarantees the degree of friend exposure as set by users. If a user only allows k of his/her friends to be disclosed, our search engine will ensure that any attempts of discovering more friends of this user through querying the user's other friends will be a failure. The key idea underlying our search engine is the construction of a unique sub social network that is capable of satisfying query needs as well as controlling the degree of friend exposure. We have carried out an extensive experimental study and the results demonstrate both efficiency and effectiveness in our approach.