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Erfani, S., Mortazavi, R. (2019). An Improved Method for Graph Data Anonymization with Emphasis on Preserving the Average Path length. Electronic and Cyber Defense, 7(2), 25-32.
S. H. Erfani; R. Mortazavi. "An Improved Method for Graph Data Anonymization with Emphasis on Preserving the Average Path length". Electronic and Cyber Defense, 7, 2, 2019, 25-32.
Erfani, S., Mortazavi, R. (2019). 'An Improved Method for Graph Data Anonymization with Emphasis on Preserving the Average Path length', Electronic and Cyber Defense, 7(2), pp. 25-32.
Erfani, S., Mortazavi, R. An Improved Method for Graph Data Anonymization with Emphasis on Preserving the Average Path length. Electronic and Cyber Defense, 2019; 7(2): 25-32.

An Improved Method for Graph Data Anonymization with Emphasis on Preserving the Average Path length

پدافند الکترونیکی و سایبری
Article 3, Volume 7, Issue 2 - Serial Number 26, July 2019, Pages 25-32    PDF (883.46 K)
Document Type: Original Article
Authors
S. H. Erfani; R. Mortazavi*
damghan university
Receive Date: 08 April 2018,  Accept Date: 13 October 2018 
Abstract
In recent decades, in view of the widespread use of graph data in different applications, for instance     in social networks, communications networks, etc. many researchers have investigated different                anonymization approaches for such data. Although relational data anonymization is mature enough, graph data anonymization is a challenging and relatively new field of research. One of the most important     anonymization models against identity disclosure risk in graph data addresses the number of links a node’s neighbors have, in the graph. In this paper, an improved method is proposed that realizes this model using both edge addition and deletion to the original graph. The application of the method to a number of        different real-world graphs confirms that the method can produce more useful graphs in terms of one of the most important characteristics in such data, i.e., the Average Path Length in the graph and graph structure will undergo less change.
 
Keywords
Privacy; Personal Data; Social Networks; Graph Anonymization
Supplementary Files
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