Artificial Intelligence Review

, Volume 47, Issue 3, pp 341–366

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

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

DOI: 10.1007/s10462-016-9484-8

Cite this article as:
Casas-Roma, J., Herrera-Joancomartí, J. & Torra, V. Artif Intell Rev (2017) 47: 341. doi:10.1007/s10462-016-9484-8


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.


Privacy k-Anonymity Randomization Social networks Graphs 

Copyright information

© Springer Science+Business Media Dordrecht 2016

Authors and Affiliations

  • Jordi Casas-Roma
    • 1
  • Jordi Herrera-Joancomartí
    • 2
  • Vicenç Torra
    • 3
  1. 1.Faculty of Computer Science, Multimedia and Telecommunications, Internet Interdisciplinary Institute (IN3)Universitat Oberta de CatalunyaBarcelonaSpain
  2. 2.Department of Information and Communications EngineeringUniversitat Autònoma de BarcelonaBellaterraSpain
  3. 3.School of InformaticsUniversity of SkövdeSkövdeSweden

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