Privacy Protection on Social Network Data Using Anonymization Methodology  
  Authors : D. Shalini Reddy; K. Narayana


Privacy is amongst the major concerns when publishing or sharing social network data for social science research and also business analysis. Recently, researchers have developed privacy models just like k-anonymity in order to avoid node reidentification through structure information. However, even though these privacy models are enforced, an attacker can always have the capacity to infer one’s information that is personal if the list of nodes largely shares the identical sensitive labels (i.e., attributes). To put it differently, the label-node relationship just isn't thoroughly protected by pure structure anonymization methods. We present privacy protection algorithms which facilitate graph data to become published within a form in a way that an adversary who possesses information regarding a node's neighborhood cannot safely infer its identity as well as sensitive labels. To the present aim, the algorithms transform an original graph in to a graph during which nodes are sufficiently indistinguishable. The algorithms are created to accomplish that while losing only small amount information and even though preserving the maximum amount of Utility as possible.


Published In : IJCAT Journal Volume 1, Issue 7

Date of Publication : 31 August 2014

Pages : 363 - 366

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Publication Link : Privacy Protection on Social Network Data Using Anonymization Methodology




D. Shalini Reddy : Post-Graduate Student, Department of Computer Science and Engineering, SIT, PUTTUR, India

K. Narayana : Head & Associate Professor, Department of Computer Science and Engineering, SIT, PUTTUR, India








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In this particular paper, k-degree-l-diversity model has implemented for privacy preserving social network data publishing. Implementation of both distinct l-diversity and recursive (c, l)-diversity also happened. So that you can attain the dependence on k-degree-l-diversity, a noise node adding algorithm to make A whole new graph through the original graph while using the constraint of introducing fewer distortions for the original graph. Rigorous analysis of the theoretical bounds within the variety of noise nodes added and their impacts by using an important graph property. Extensive experimental results demonstrate how the noise node adding algorithms can performs a much better result compared to the previous work of edge editing method. In a very distributed environment, data publication satisfy certain privacy requirements, a hacker can still collapse privacy by connecting the information by different users. Similar Protocols needs to be developed to conserve the data publishers to assure the Privacy preservation.










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