TSRAM: A time-saving k-degree anonymization method in social network

Abstract

Social networks provide an attractive environment in order to have low cost and easy communication; however, analyzing huge amounts of produced data can considerably affect the user's privacy. In other words, an efficient algorithm should intelligently take the user's privacy into account while extracting data for useful information. In recent years, many studies have been conducted on social network privacy preservation for data publishing. However, the current algorithms are not one-time scan; that is, for every level of anonymization, the data set must be scanned again and this is a time-consuming operation. In order to address the above mentioned issue, the present research introduces a time-saving k-degree anonymization method in social network (TSRAM) that anonymizes the social network graph without having to rescan the data set for different levels of anonymity. First, it produces a tree from the data set. Then, the anonymized degree sequence of the graph is computed based on the tree. The proposed method employs an efficient approach to partition the node degrees. It takes advantage of partitioning the graph bottom-up nodes based on the anonymization levels. Moreover, it uses two effective criteria to increase the utility of the anonymized graph. Comparing to other similar techniques, the results show that TSRAM is effective, not only to make the degree sequence anonymization of the graph one-time scan, but also to preserve the utility of the anonymized graph.