Background knowledge based privacy metric model for online social networks

The data of online social network (OSN) is collected currently by the third party for various purposes. One of the problems in such practices is how to measure the privacy breach to assure users. The recent work on OSN privacy is mainly focus on privacy-preserving data publishing. However, the work on privacy metric is not systematic but mainly focus on the traditional datasets. Compared with the traditional datasets, the attribute types in OSN are more diverse and the tuple is relevant to each other. The retweet and comment make the graph character of OSN notably. Furthermore, the open application programming interfaces (APIs) and lower register barrier make OSN open environment, in which the background knowledge is more easily achieved by adversaries. This paper analyzes the background knowledge in OSN and discusses its characteristics in detail. Then a privacy metric model faces OSN background knowledge based on kernel regression is proposed. In particular, this model takes the joint attributes and link knowledge into consideration. The effect of different data distributions is discussed. The real world data set from weibo.com has been adopted. It is demonstrated that the privacy metric algorithm in this article is effective in OSN privacy evaluation. The prediction error is 30% lower than that of the work mentioned above