Anonymization of centralized and distributed social networks by sequential clustering

Tamir Tassa, Dror J. Cohen

Research output: Contribution to journalArticlepeer-review

93 Scopus citations

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.

Original languageEnglish
Article number6081867
Pages (from-to)311-324
Number of pages14
JournalIEEE Transactions on Knowledge and Data Engineering
Volume25
Issue number2
DOIs
StatePublished - 4 Jan 2013
Externally publishedYes

Keywords

  • Social networks
  • clustering
  • distributed computation
  • privacy preserving data mining

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