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Analyzing Network Privacy Preserving Methods: A Perspective of Social Network Characteristics
IEICE TRANSACTIONS on Information and Systems
Publication Date: 2014/06/01
Online ISSN: 1745-1361
Type of Manuscript: LETTER
Category: Artificial Intelligence, Data Mining
network privacy preserving, privacy breach, structural disparity,
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A burst of social network services increases the need for in-depth analysis of network activities. Privacy breach for network participants is a concern in such analysis efforts. This paper investigates structural and property changes via several privacy preserving methods (anonymization) for social network. The anonymized social network does not follow the power-law for node degree distribution as the original network does. The peak-hop for node connectivity increases at most 1 and the clustering coefficient of neighbor nodes shows 6.5 times increases after anonymization. Thus, we observe inconsistency of privacy preserving methods in social network analysis.