A survey of graph-modification techniques for privacy-preserving on networks

Jordi Casas-Roma, Jordi Herrera-Joancomartí, Vicenç Torra

Research output: Contribution to journalArticleResearchpeer-review

56 Citations (Scopus)


© 2016, Springer Science+Business Media Dordrecht. Recently, a huge amount of social networks have been made publicly available. In parallel, several definitions and methods have been proposed to protect users’ privacy when publicly releasing these data. Some of them were picked out from relational dataset anonymization techniques, which are riper than network anonymization techniques. In this paper we summarize privacy-preserving techniques, focusing on graph-modification methods which alter graph’s structure and release the entire anonymous network. These methods allow researchers and third-parties to apply all graph-mining processes on anonymous data, from local to global knowledge extraction.
Original languageEnglish
Pages (from-to)341-366
JournalArtificial Intelligence Review
Issue number3
Publication statusPublished - 1 Mar 2017


  • Graphs
  • k-Anonymity
  • Privacy
  • Randomization
  • Social networks


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