Sequential Clustering Algorithms for Anonymizing Social Networks

The privacy-preservation in social networks is major problem in now-a-days. In distributed setting the complex data is divide between several data holders. The target is to appear at an anonymized view of the unified network without illuminating to any of the data holders information about links between nodes that are hold by other data holders. To that finish, in centralized setting two variants of an anonymization algorithm are offered which is based on sequential clustering (Sq). Proposed algorithms substantially break the SaNGreeA algorithm due to Campan and Truta which is the primary algorithm for achieving anonymity in networks by means of clustering and then secure distributed versions of algorithms. To the top of awareness, this is the earliest study of privacy preservation in distributed social networks. Finally, conclude by outlining potential research proposals in that path. Keywords—social networks, clustering, privacy preservation, distributed computation I. RELATED WORK Now-a-days the use of social networks among the people has become more popular. With the impact of social networks on society, the people become more sensitive regarding privacy issues in the common networks and most sociologists agree that this trend will not fade away. Privacy preservation in social networks is major problem in now-a-days. This problem can be solved as explained follows. The advent of social network sites in the last years seems to be a trend that will likely continue. What naive technology users may not realize is that the information they provide online is stored implications of massive data gathering, and effort has been made to protect the data from unauthorized disclosure. However, the data privacy research has mostly targeted traditional data models such as micro data. Recently, social network data has begun to be analyzed from a specific privacy perspective, one that considers, besides the attribute values that characterize the individual entities in the networks, their relationships with other entities. Campan and Truta[1] proposed a greedy algorithm for anonymizing social network and a measure that quantifies the information loss in the anonymization process due to edge generalization. Publishing data about individuals without revealing sensitive information about them is an important problem. In recent years, a new definition of privacy called kanonymity has gained popularity. In a k-anonymized dataset, each record is indistinguishable from at least k − 1 other records with respect to certain “identifying” attributes. Two simple attacks that a k-anonymized dataset has some subtle, but severe privacy problems. First, an attacker can discover the values of sensitive attributes when there is little diversity in those sensitive attributes. This is a known problem. Second, attackers often have background knowledge, and A.Machanavajjhala, D.Kifer, J.Gehrke and M.Venkitasubramaniam [2] show that k-anonymity does not guarantee privacy against attackers using background knowledge. They give a detailed analysis of these two attacks and propose a novel and powerful privacy criterion called l-diversity that can defend against such attacks. In addition to building a formal foundation for l-diversity, they show in an experimental evaluation that l-diversity is practical and can be implemented efficiently. One of the most well studied models of privacy preservation is k-anonymity. Previous studies of kanonymization used various utility measures that aim at enhancing the correlation between the original public data and generalised public data. J.Goldberger and T.Tassa [3] bearing in mind that a primary goal in releasing the anonymized database for data mining is to deduce methods of predicting the private data from the public data, and propose a new information-theoretic measure that aims at enhancing the correlation between the generalised public data and private data. Such a measure significantly enhances utility of the released anonymized database for data mining. They proceed to describe a new algorithm that is designed to achieve k-anonymity with high utility, independently of the underlying utility measure . that algorithm is based on a modified version of sequential clustering which is the method of choice in clustering. Expreimental comparison with four well known algorithms of k-anonymity show that the sequential clustering algorithm is an efficient algorithm that achieves the best utility results. They describe a modification of the algorithm that outputs k-anonymizations which respect the additional security measure of l-diversity. Consider the distributed setting in which the network data is split between several data holders. The goal is to arrive at an anonymized view of the unified network without revealing to any of the data holders information about links between nodes that are controlled by other data holders . Algorithms significantly outperform SaNGreeA algorithm due to Campan and Truta which is the leading algorithm P.Mohana Lakshmi et al, / (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (4) , 2014, 5647-5650