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Title: A survey of graph-modification techniques for privacy-preserving on networks
Author: Casas Roma, Jordi
Herrera Joancomartí, Jordi
Torra Reventós, Vicenç
Others: Universitat Oberta de Catalunya. Internet Interdisciplinary Institute (IN3)
Universitat Autònoma de Barcelona
University of Skövde
Keywords: privacy
k-anonymity
randomization
social networks
graphs
Issue Date: Mar-2017
Publisher: Artificial Intelligence Review
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
Also see: https://doi.org/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.
Language: English
URI: http://hdl.handle.net/10609/77625
ISSN: 0269-2821
Appears in Collections:Articles

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