Anonymization in Social Ne tworks: A Survey on the issues of Data Privacy in Social Network Sites

Abstract

Recent years have seen unprecedented growth in the popularity of social network systems. Social networks are online applications which allow its users to connect by various link types. Online social networks, such as Facebook, LinkedIn are progressively utilized by many people. It makes digital communication technologies sharpening tools for extending the social circle of people. It has already become an important integral part of our daily lives, enabling us to contact our friends and families on time. As social networks have developed rapidly, recent research has begun to explore social networks to understand their structure, advertising and marketing, and data mining online population, representing 1.2 billion users around the world. More and more social network data has been made publicly available and analyzed in one way or another. But some of the information revealed is meant to be private hence social network data has led to the risk of leakage of confidential information of individuals. However, privacy concerns can be used to prevent these efforts. Privacy pre- serving publishing of social network data becomes a more and more important concern. This leads for privacy-preserving social network data mining, which is the discovery of information and relationships from social network data without violating privacy. Privacy in online social networks data has been of utmost concern. Hence, the research in this field is still in its early years. Privacy preservation is a significant research issue in social networking. Since more personalized information is shared with the public, violating the privacy of a target user become much easier. We argue that the different privacy problems are entangled and that research on privacy in OSNs would benefit from a more holistic approach. In this paper, after an overview on different research subareas of SNSs, we will get more focused on the subarea of privacy protection in social network and we present a brief yet systematic review of the existing anonymization techniques for privacy preserving publishing of social net- work data.