Camouflaged Variational Graph AutoEncoder Against Attribute Inference Attacks for Cross-Domain Recommendation

Cross-domain recommendation (CDR) aims to alleviate the data sparsity problem by leveraging the benefits of modeling two domains. However, existing research often focuses on the recommendation performance while ignores the privacy leakage issue. We find that an attacker can infer user attribute info...

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Bibliographische Detailangaben

Veröffentlicht in:	IEEE transactions on knowledge and data engineering Jg. 37; H. 7; S. 3916 - 3932
Hauptverfasser:	Xiong, Yudi, Guo, Yongxin, Pan, Weike, Yang, Qiang, Ming, Zhong, Zhang, Xiaojin, Han, Yu, Lin, Tao, Tang, Xiaoying
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	IEEE 01.07.2025
Schlagworte:	Accuracy attribute inference attacks Autoencoders Computational modeling cross-domain recommendation (CDR) Data models Graph convolutional networks Information leakage Knowledge transfer Privacy Protection recommendation system Recommender systems Variational autoencoders
ISSN:	1041-4347, 1558-2191
Online-Zugang:	Volltext
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