An Overview About Privacy Protection of Facebook Social Network Users Data

Abstract

Graph anonymization is a necessary process before any publication of data from a social network in order to preserve the privacy of individuals, especially when these networks contain sensitive or personally identifiable information, such as social contacts, personal opinions and recordings of private communications. So, this publication can therefore constitute a threat to privacy. Generally, this simple procedure is not sufficient, some inference attacks on the published graph can lead to de-anonymize certain nodes, to learn the existence of a social relation between two nodes or to use the structure of the graph itself to deduce the value of certain sensitive attributes. For this reason, several anonymization techniques were proposed in the literature to combat this problem, they modify the structure of the social graph by adding and/or removing nodes and/or edges, these methods can be classified as Graph Modification, Generalization or Clustering and Differential Privacy Models. In this paper, we present a brief yet systematic review of the existing anonymization techniques for privacy preserving publishing of social network data. We identify the best graph anonymization method that ensure a high confidentiality level after making a clear comparison between them. Finally, we present our perspective or our proposition that still under development which is based on differential privacy method and attacking the graph processing in different ways at the same time, graph structure and node attributes.