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Privacy Preserving Based on Vector SimiIarity for Weighted SociaI Networks
Author(s): 
Pages: 1568-1574
Year: Issue:  8
Journal: Acta Electronica Sinica

Keyword:  social networksedge weightprivacy preservingvector set modelweighted Euclidean distance;
Abstract: Aiming at the publication of weighted social networks,a random perturbation method based on vector similarity is proposed.It can protect network structures and edge weights in multiple release scenarios.It constructs vector set models by segmen-tation based on vertex cluster using edge space theory.It adopts weighted Euclidean distance as similarity metrics to construct the re-leased candidate sets according to the threshold.It randomly selects vectors from candidate sets to construct the published weighted social networks.The proposed method can resist multiple vertex recognition attacks,force attackers to re-identify in a large result set that the existential probabilities of the vectors are same,and increase the uncertainty of recognition.The experimental results demon-strate that it can preserve individuals’privacy security,meanwhile it can protect some structure characteristics for networks analysis and improve data utility.
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