An efficient privacy protection in mobility social network services with novel clustering-based anonymization

A popular means of social communication for online users has become a trend with rapid growth of social networks in the last few years. Facebook, Myspace, Twitter, LinkedIn, etc. have created huge amounts of data about interactions of social networks. Meanwhile, the trend is also true for offline scenarios with rapid growth of mobile devices such as smart phones, tablets, and laptops used for social interactions. These mobile devices enlarge the traditional social network services platform and lead to a greater amount of mobile social network data. These data contain more private information of individuals such as location, habit, and health condition. However, there are many analytical, sociological, and economic questions that can be answered using these data, so the mobility data managers are expected to share the data with researchers, governments, and/or companies. Therefore, mobile social network data is badly in need of anonymization before it is shared or analyzed widely. k-anonymization is a well-known clustering-based anonymization approach. However, the implementation of this basic approach has been a challenge since many of the mobile social network data involve categorical data values. In this paper, we propose an approach for categorical data clustering using rough entropy method with DBSCAN clustering algorithm to improve the performance of k-anonymization approach. It has the ability to deal with uncertainty in the clustering process and can effectively find arbitrarily shaped clusters. We will report the proposed approach and discuss the credibility by theoretical studies and examples. And experimental results on two benchmark data sets obtained from UCI Machine Learning Repository show that our approach is second to none among the Fuzzy Centroids, MMeR, SDR and ITDR, etc. with respect to the local and global purity of clusters. Since the clustering algorithm is a key point of k-anonymization for clustering mobile social network data, our experimental results show that our proposed algorithm can be more effective to balance the utility of the mobile social network data and the performance of anonymization.