Affiliations: Department of Computer Science and Engineering, VIT University, Vellore, Tamil Nadu
Correspondence:
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Corresponding author: Saptarshi Chakraborty, Department of Computer Science and Engineering, VIT University, Vellore, Tamil Nadu. E-mail:sssaptarshii@gmail.com
Abstract: Large amounts of data generated everyday by different organizations for
various purposes have catalyzed research opportunities related to data
science. Publishing raw data may raise security concerns among the users or
actors who have provided some sensitive information in the raw data. Over
the years, it has been observed that attackers can very easily exploit the
sensitive information as well as the identity of the users from the raw
data. Thus, to protect the identity of the users in the anonymized data, the
notion of k-anonymity and its improved version l-diversity have been proposed.
Several algorithms have been developed to achieve k-anonymity as well as
l-diversity for both relational micro-data and social network data. In this
paper, we propose an approach based on the eigenvector centrality value of
the individual nodes to achieve k$-anonymity as well as $l$-diversity by adding
noise nodes in the raw data. In the process of adding noise nodes, we
focused on adding noise nodes in such an intelligent manner so that they
have very little influence on the anonymized data. Our proposed algorithm
also ensures minimal changes in the social influence of the nodes in the
anonymized data which are already present in the raw data. Through various
measures and experiments, we establish the effectiveness of our proposed
algorithm over the several existing social network anonymization techniques.
Keywords: Social network, k-anonymity, l$-diversity, eigenvector centrality, noise node, data privacy
