A γ‐Strawman privacy‐preserving scheme in weighted social networks

Abstract

With the dramatic development of social network applications, such as the Facebook, Twitter, and MySpace, privacy‐preserving problem is getting increasingly concerned. Apart from node information, researchers have found that structure information can also leak data providers' privacy, especially in weighted social networks. However, most of the existing private‐preserving schemes focus on a single aspect. The comprehensive consideration introduces two challenges. On one hand, the different anonymity demands of node and structure information lead to the collision of different design criteria, which is called as consistency matching problem. On the other hand, the simple combination of existing schemes may introduce large amounts of unnecessary changes, which makes the published information meaningless. Thus, we must find the balance between anonymity demands and changes, which is called as optimization trade‐off problem. In this paper, we propose a γ‐Strawman privacy‐preserving scheme in weighted social networks to solve these challenges. To address consistency matching problem, we propose a greedy algorithm based on a user trade‐off metric. For optimization tradeoff problem, a closeness edge‐editing technology is considered, which can change the private information slightly. Finally, we evaluate our scheme on real‐world datasets, the experimental results show that the γ‐Strawman scheme is efficient. Copyright © 2017 John Wiley & Sons, Ltd.