A brief survey on anonymization techniques for privacy preserving publishing of social network data

Nowadays, partly driven by many Web 2.0 applications, more and more social network data has been made publicly available and analyzed in one way or another. Privacy pre-serving publishing of social network data becomes a more and more important concern. In this paper, we present a brief yet systematic review of the existing anonymization techniques for privacy preserving publishing of social net-work data. We identify the new challenges in privacy pre-serving publishing of social network data comparing to the extensively studied relational case, and examine the pos-sible problem formulation in three important dimensions: privacy, background knowledge, and data utility. We sur-vey the existing anonymization methods for privacy preser-vation in two categories: clustering-based approaches and graph modification approaches. 1