Please use this identifier to cite or link to this item:
Title: A summary of k-degree anonymous methods for privacy-preserving on networks
Author: Casas Roma, Jordi
Herrera Joancomartí, Jordi
Torra Reventós, Vicenç
Others: Universitat Autònoma de Barcelona
Artificial Intelligence Research Institute
Universitat Oberta de Catalunya (UOC)
Keywords: privacy
social networks
information loss
data utility
Issue Date: 22-Aug-2014
Publisher: Studies in Computational Intelligence
Citation: Casas-Roma, J., Herrera-Joancomartí, J. & Torra, V. (2015). A summary of k-degree anonymous methods for privacy-preserving on networks. Studies in Computational Intelligence, 567(), 231-250. doi: 10.1007/978-3-319-09885-2_13
Project identifier: info:eu-repo/grantAgreement/TIN2011-27076-C03-02
Also see:
Abstract: In recent years there has been a significant raise in the use of graph-formatted data. For instance, social and healthcare networks present relationships among users, revealing interesting and useful information for researches and other third-parties. Notice that when someone wants to publicly release this information it is necessary to preserve the privacy of users who appear in these networks. Therefore, it is essential to implement an anonymization process in the data in order to preserve users' privacy. Anonymization of graph-based data is a problem which has been widely studied last years and several anonymization methods have been developed. In this chapter we summarize some methods for privacy-preserving on networks, focusing on methods based on the k-anonymity model. We also compare the results of some k-degree anonymous methods on our experimental set up, by evaluating the data utility and the information loss on real networks.
Language: English
ISSN: 1860-949XMIAR

Appears in Collections:Articles

Files in This Item:
File SizeFormat 
Summarykdegree.pdf322.42 kBAdobe PDFView/Open

Items in repository are protected by copyright, with all rights reserved, unless otherwise indicated.