IEEE Access Special Section: Privacy Preservation for Large-Scale User Data in Social Networks

Abstract

Social networks have become one of the most popular platforms for people to communicate and interact with their friends and share personal information and experiences (e.g., Facebook owns over 1.23 billion monthly active users). The increasing popularity of social networks has generated extremely large-scale user data (e.g., Twitter generates 500 million tweets per day and around 200 billion tweets per year). These data can help improve people’s quality of life as well as benefit various interest groups such as advertisers, application developers, and so on. However, privacy may be compromised if learning algorithms are used to infer unpublished privacy information from published data. Hence, user data privacy preservation has become one of the most urgent research issues in social networks.