Defense against Membership Inference Attack Applying Domain Adaptation with Addictive Noise

ISSN Print: 2327-5219
ISSN Online: 2327-5227
www.scirp.net/journal/jcc
E-mail: [email protected]

Citations

"Defense against Membership Inference Attack Applying Domain Adaptation with Addictive Noise"
written by Hongwei Huang,
published by Journal of Computer and Communications, Vol.9 No.5, 2021

has been cited by the following article(s):

[1]	Mitigating Privacy Risk in Membership Inference by Convex-Concave Loss
	arXiv preprint arXiv:2402.05453, 2024

[2]	Defenses to membership inference attacks: A survey
	ACM Computing …, 2023

[3]	Defending against membership inference attacks with high utility by GAN
	IEEE Transactions on …, 2022

[4]	BAN-MPR: Defending against Membership Inference Attacks with Born Again Networks and Membership Privacy Regularization
	2022 International Conference on …, 2022

[5]	Membership Feature Disentanglement Network
	Proceedings of the 2022 ACM on Asia …, 2022

[1]	Transformer-Based Sensor Fusion for Autonomous Vehicles: A Comprehensive Review IEEE Access, 2025 DOI:10.1109/ACCESS.2025.3545032

[2]	Defenses to Membership Inference Attacks: A Survey ACM Computing Surveys, 2024 DOI:10.1145/3620667

[3]	Defending Against Membership Inference Attacks With High Utility by GAN IEEE Transactions on Dependable and Secure Computing, 2023 DOI:10.1109/TDSC.2022.3174569

[4]	Membership Feature Disentanglement Network Proceedings of the 2022 ACM on Asia Conference on Computer and Communications Security, 2022 DOI:10.1145/3488932.3497772

[5]	BAN - MPR: Defending against Membership Inference Attacks with Born Again Networks and Membership Privacy Regularization 2022 International Conference on Computers and Artificial Intelligence Technologies (CAIT), 2022 DOI:10.1109/CAIT56099.2022.10072217