Volume : III, Issue : V, May - 2014

Privacy in Social Network Data by Anonymization and Protection of Sensitive Labels

Arathy G, S. Kavitha Murugesan

Abstract :

Privacy is one important factor to achieve privacy for social science research and business analysis. The problem occurs for publishing and shå social network data, where privacy is a major concern. Many models has been developed to prevent node reidentification  through structure information ,but still an attacker may be able to infer ones private information if a group of nodes largely share the same sensitive labels. The problem with existing approach is that there are chances which alter the graph properties–degree L–diversity model is defined here. The k–anonymity privacy requirement for publishing microdata requires that each equivalence class (i.e., a set of records that are indistinguishable from each other with respect to certain “identifying” attributes) contains at least k records. Recently, several authors have recognized that k–anonymity cannot prevent attribute disclosure. The notion of  diversity has been Proposed to address this diversity requires that each equivalence class has at least  well–represented values for each sensitive attribute. This method considers protection of structural information and sensitive labels of individuals. The methodology is based on adding noise nodes. Here a new algorithm is defined, in which noise nodes are added to the original graph and this is done with least distortion to graph properties. In order to evaluate the effectiveness of proposed technique extensive experiments are done.

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Article: Download PDF   DOI : 10.36106/ijsr  

Cite This Article:

Arathy G, S.Kavitha Murugesan Privacy in Social Network Data by Anonymization and Protection of Sensitive Labels International Journal of Scientific Research, Vol.III, Issue. V, May 2014

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