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

Artificial Intelligence Review volume 47, pages 341–366 (2017)Cite this article

Abstract

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.

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Acknowledgments

This work was partly funded by the Spanish Government through projects TIN2011-27076-C03-02 “CO-PRIVACY”, TIN2014-57364-C2-2-R “SMARTGLACIS” and TIN2014-55243-P and the Catalan AGAUR Grant 2014SGR-691.

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Authors and Affiliations

  1. Faculty of Computer Science, Multimedia and Telecommunications, Internet Interdisciplinary Institute (IN3), Universitat Oberta de Catalunya, Barcelona, Spain

    Jordi Casas-Roma

  2. Department of Information and Communications Engineering, Universitat Autònoma de Barcelona, Bellaterra, Spain

    Jordi Herrera-Joancomartí

  3. School of Informatics, University of Skövde, Skövde, Sweden

    Vicenç Torra

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  1. Jordi Casas-Roma
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  2. Jordi Herrera-Joancomartí
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  3. Vicenç Torra
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Correspondence to Jordi Casas-Roma.

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Casas-Roma, J., Herrera-Joancomartí, J. & Torra, V. A survey of graph-modification techniques for privacy-preserving on networks. Artif Intell Rev 47, 341–366 (2017). https://doi.org/10.1007/s10462-016-9484-8

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