Abstract: Social network has become part of people’s daily life recently,and brings convenience to our social activities.However,it poses threats to our personal privacy at the same time.Usually,people want to protect part of their private social activity information to prevent relatives,friends,colleagues or other specific groups from visiting.One common protective method is to socialize anonymously.And some social networks provide anonymity mechanisms for users,allowing them to hide some private information about social activities,thus separating these social activities from the main account.In addition,users can create alternate accounts and set different attributes,friendships to achieve the same aim.This paper proposed a topic-based re-identification method for social network users to make an attack on these protection mechanisms.The text contents published by anonymous users (or alternate accounts) and non-anonymous users (main accounts) are analyzed based on topic model.And the time factor and text length factor are introduced to construct user profiles in order to improve the accuracy ofthe proposed method.Then the similarity between anonymous and non-anonymous user profiles is analyzed to match their identities.Finally,experiments on real social network dataset show that the proposed method can effectively improve the accuracy of re-identification for users in social networks.

Key words: Anonymity, Big data, Privacy protection, Re-identification, Social networks