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

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Erfani s.h.; MORTAZAVI R.

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Paper Information

Journal: JOURNAL OF ELECTRONIC AND CYBER DEFENCE Year:2019 | Volume:7 | Issue:2 (26) Page(s): 25-32

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Information Journal Paper

Title

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

Author(s)

Erfani s.h. | MORTAZAVI R. | Issue Writer Certificate

Keywords

PrivacyQ1

Personal DataQ1

Social NetworksQ1

Graph AnonymizationQ1

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.

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APA: Copy

Erfani, s.h., & MORTAZAVI, R.. (2019). An Improved Method for Graph Data Anonymization with Emphasis on Preserving the Average Path Length. JOURNAL OF ELECTRONIC AND CYBER DEFENCE, 7(2 (26) ), 25-32. SID. https://sid.ir/paper/243226/en

Vancouver: Copy

Erfani s.h., MORTAZAVI R.. An Improved Method for Graph Data Anonymization with Emphasis on Preserving the Average Path Length. JOURNAL OF ELECTRONIC AND CYBER DEFENCE[Internet]. 2019;7(2 (26) ):25-32. Available from: https://sid.ir/paper/243226/en

IEEE: Copy

s.h. Erfani, and R. MORTAZAVI, “An Improved Method for Graph Data Anonymization with Emphasis on Preserving the Average Path Length,” JOURNAL OF ELECTRONIC AND CYBER DEFENCE, vol. 7, no. 2 (26) , pp. 25–32, 2019, [Online]. Available: https://sid.ir/paper/243226/en

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