Abstract: Networks structure describes a set of entities and the relations between them. A social network, for example, delivers information on persons in some population and the relations between them, which may describe associations of collaboration, friendship, correspondence and so on. An information network, as extra example, may define technical publications and their reference links. In their most rudimentary form, networks are modeled by a graph, where the nodes of the graph denote to the entities, while edges denote relations between them. Factual social networks may be more composite or contain additional information. Such social networks are of interest to researchers from many disciplines, be it sociology, psychology, epidemiology, or market explore. Nevertheless, the data in such social network cannot be released as such, because it may contain sensitive information. The difficulties of privacy-preservation in social networks rises severely now. We consider the spreaded setting in which the network data is divided between several data holders. The data is divided between a numbers of data holders. The idea is to get there at an anonymized view of the joint network without informative to any of the data holders. A variant of an anonymization algorithm which is based on clustering is used. High sensitive data has been secured in l-diversity algorithm. It provides the learning of confidentiality protection in spread common networks.
Keywords: Social Network, Anonymization, Clustering, Privacy Preserving Data.
Title: ANONYMIZATION OF FUNDAMENTAL AND SPREAD SOCIAL NETWORK USING CLUSTERING
Author: G.SAHANA, M.MAHESWARI, S.SAHANA, R.LATHA
International Journal of Computer Science and Information Technology Research
ISSN 2348-120X (online), ISSN 2348-1196 (print)
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