Anonymized Social Networks Community Preservation

Abstract

Abstract-Social Networks have been widely used in the society. Most of the people are connected to one another, communicated with each other and share the information in different forms. The information gathered from different social networking sites is growing tremendously in large volumes of various research, marketing and other purposes which is creating security and privacy concerns. The gathered information contains some sensitive and private information about an individual, such as the relationship of an individual or group information. So, to protect the data from unauthorized users the data should be anonymized before publishing. In this paper, we study how the k-degree and k-NMF anonymized methods preserve the existing communities of the original social networks. We use an existing heuristic algorithm called Louvian method to identify the communities in social networks. We conduct the experiments on real data sets and compare the performances of the two anonymized social networks for preservation of communities of the original social networks.

An Improved K-anonymization Approach for Preserving Graph Structural Properties

“…In this paper, we focus on previous studies that addressed the anonymization problem through Edge modification-based approaches. Some authors concentrate on preserving the general structural properties of the anonymized network [20]- [22], While others are interested in preserving the community structure in the anonymized version [23], [24].…”