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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Vydané v:	IEEE transactions on knowledge and data engineering Ročník 37; číslo 7; s. 3916 - 3932
Hlavní autori:	Xiong, Yudi, Guo, Yongxin, Pan, Weike, Yang, Qiang, Ming, Zhong, Zhang, Xiaojin, Han, Yu, Lin, Tao, Tang, Xiaoying
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	IEEE 01.07.2025
Predmet:	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
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