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ECWAY TECHNOLOGIES
IEEE PROJECTS & SOFTWARE DEVELOPMENTS
OUR OFFICES @ CHENNAI / TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE
CELL: +91 98949 17187, +91 875487 2111 / 3111 / 4111 / 5111 / 6111
VISIT: www.ecwayprojects.com MAIL TO: ecwaytechnologies@gmail.com

ANONYMIZATION OF CENTRALIZED AND DISTRIBUTED SOCIAL
NETWORKS BY SEQUENTIAL CLUSTERING

ABSTRACT:

We study the problem of privacy-preservation in social networks. We 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. To that end, we start with the
centralized setting and offer two variants of an anonymization algorithm which is based on
sequential clustering (Sq).

Our algorithms significantly outperform the SaNGreeA algorithm due to Campan and Truta
which is the leading algorithm for achieving anonymity in networks by means of clustering. We
then devise secure distributed versions of our algorithms. To the best of our knowledge, this is
the first study of privacy preservation in distributed social networks. We conclude by outlining
future research proposals in that direction.

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Dotnet anonymization of centralized and distributed social networks by sequential clustering

  • 1. ECWAY TECHNOLOGIES IEEE PROJECTS & SOFTWARE DEVELOPMENTS OUR OFFICES @ CHENNAI / TRICHY / KARUR / ERODE / MADURAI / SALEM / COIMBATORE CELL: +91 98949 17187, +91 875487 2111 / 3111 / 4111 / 5111 / 6111 VISIT: www.ecwayprojects.com MAIL TO: ecwaytechnologies@gmail.com ANONYMIZATION OF CENTRALIZED AND DISTRIBUTED SOCIAL NETWORKS BY SEQUENTIAL CLUSTERING ABSTRACT: We study the problem of privacy-preservation in social networks. We 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. To that end, we start with the centralized setting and offer two variants of an anonymization algorithm which is based on sequential clustering (Sq). Our algorithms significantly outperform the SaNGreeA algorithm due to Campan and Truta which is the leading algorithm for achieving anonymity in networks by means of clustering. We then devise secure distributed versions of our algorithms. To the best of our knowledge, this is the first study of privacy preservation in distributed social networks. We conclude by outlining future research proposals in that direction.