Cross-platform Network User Alignment Interference Methods Based on Obfuscation Strategy

Luyao Wang, Yan Liu, Xiaoyu Guo, Ziqi Long, Chunfang Yang

Abstract: The widespread usage of user alignment technology raises a number of security and privacy concerns. This study suggests cross-platform network user alignment interference methods based on obfuscation strategy to mitigate the risk of significant protection of user social identity, which is easily mined jointly, for the cross-platform network user alignment algorithm based on social relationship.In order to decrease the accuracy of the classical algorithm for cross-platform alignment and safeguard user privacy, this paper suggests three interference methods based on obfuscation strategy: virtual user deployment strategy, virtual connection deployment strategy, and connection deletion strategy. These methods lower the risk of co-mining significant protected users’ social identities by deploying virtual users, deploying virtual connection relations, and deleting connection relations.This research identifies interference efficiency as appropriate evaluation metric. It is tested on Domain-adversarial Network Alignment and User Identity Linking Algorithm Based on Graph Auto-Encoders using the Foursquare-Twitter social network dataset. When five virtual nodes are added, the virtual user deployment strategy achieves up to 84% interference efficiency; when five similar nodes are added, the virtual connection deployment strategy achieves up to 82% interference efficiency; and when 90% of the target node’s connections are deleted, the connection deletion strategy achieves 80% interference efficiency. All three options offer effective means of protecting user privacy. In the process of actual implementation, it is vital to select the best technique based on the particular situation.

External IDs:dblp:conf/trustcom/WangLGLY24