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 preserving 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 network data. We identify the new challenges in privacy preserving publishing of social network data comparing to the extensively studied relational case, and examine the possible problem formulation in three important dimensions: privacy, background knowledge, and data utility. We survey the existing anonymization methods for privacy preservation in two categories: clustering-based approaches and graph modification approaches.

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A statistical model is presented for the investigation of a practical method used in relevance feedback. A necessary and sufficient condition for the two parameters used in this method to define a better query than the original query is given. A region in the plane of the parameters is shown to satisfy the sufficient condition. While the points for producing optimal queries are not exactly located, they are shown to be lying on a finite portion of a hyperbola. Experimental results support some of the theoretical findings.