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

TLDR

Social network data is increasingly publicly available and analyzed, raising growing concerns about privacy preservation. The paper systematically reviews anonymization techniques for social network data, identifies new challenges relative to relational data, and explores problem formulations across privacy, background knowledge, and data utility. The review covers anonymization methods grouped into clustering‑based and graph modification approaches.

Abstract

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.