A Survey on Attack Prevention and Handling Strategies in Graph Based Data Anonymization

Abstract: Current days have seen a steep rise in the need for the social network data to be published in public. With the rapid growth of web application development the need for private data to be published has grown multi fold. Representing the individual sensitive labels in a graphical structure for the ease of access and quick re-identification is an important issue to be addressed. In this paper, we have made a detailed surveyed about the existing techniques that preserve the sensitive data in social network data. It is pragmatic that preserving the graph structure and label re-identification by adding some noise nodes to the graph makes difference in degree is inferred from literature. Several techniques dealt in literature to deal with abnormal graph structure are studied in detail. The various possible attacks on social network data and techniques to prevent and handle the same are studied in detail.

Keywords: Privacy, Graphical Data, Re-identification, Attack, Data Security, Anonymization