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Anonymization of Centralized and Distributed Social Networks by Sequential Clustering
Feb. 2013 (vol. 25 no. 2)
pp. 311-324
Tamir Tassa, The Open University, Ra'anana, Israel
Dror J. Cohen, The Open University, Ra'anana, Israel
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.
Index Terms:
Clustering algorithms,Social network services,Loss measurement,Tin,Algorithm design and analysis,Partitioning algorithms,distributed computation,Social networks,clustering,privacy preserving data mining
Citation:
Tamir Tassa, Dror J. Cohen, "Anonymization of Centralized and Distributed Social Networks by Sequential Clustering," IEEE Transactions on Knowledge and Data Engineering, vol. 25, no. 2, pp. 311-324, Feb. 2013, doi:10.1109/TKDE.2011.232
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