Please use this identifier to cite or link to this item: http://hdl.handle.net/10609/77625
Title: A survey of graph-modification techniques for privacy-preserving on networks
Author: Casas-Roma, Jordi  
Herrera-Joancomartí, Jordi  
Torra, Vicenç
Others: Universitat Oberta de Catalunya. Internet Interdisciplinary Institute (IN3)
Universitat Autònoma de Barcelona (UAB)
University of Skövde
Citation: Casas-Roma, J., Herrera-Joancomartí, J. & Torra, V. (2017). A survey of graph-modification techniques for privacy-preserving on networks. Artificial Intelligence Review, 47(3), 341-366. doi: 10.1007/s10462-016-9484-8
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.
Keywords: privacy
k-anonymity
randomization
social networks
graphs
DOI: 10.1007/s10462-016-9484-8
Document type: info:eu-repo/semantics/article
Version: info:eu-repo/semantics/acceptedVersion
Issue Date: Mar-2017
Publication license: http://creativecommons.org/licenses/by-nc-nd/3.0/es/  
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