Membership Feature Disentanglement Network

Author:

Ha Heonseok1,Jang Jaehee1,Jeong Yonghyun2,Yoon Sungroh1

Affiliation:

1. Seoul National University, Seoul, South Korea

2. Samsung SDS, Seoul, South Korea

Funder

National Research Foundation of Korea

Publisher

ACM

Reference49 articles.

1. Membership Privacy in MicroRNA-based Studies

2. Daniel Bernau , Philip-William Grassal , Jonas Robl , and Florian Kerschbaum . 2019. Assessing Differentially Private Deep Learning with Membership Inference. arXiv preprint arXiv:1912.11328 ( 2019 ). Daniel Bernau, Philip-William Grassal, Jonas Robl, and Florian Kerschbaum. 2019. Assessing Differentially Private Deep Learning with Membership Inference. arXiv preprint arXiv:1912.11328 (2019).

3. GAN-Leaks: A Taxonomy of Membership Inference Attacks against Generative Models

4. Junjie Chen , Wendy Hui Wang , and Xinghua Shi . 2020 a . Differential Privacy Protection Against Membership Inference Attack on Machine Learning for Genomic Data. In BIOCOMPUTING 2021: Proceedings of the Pacific Symposium. World Scientific, 26--37 . Junjie Chen, Wendy Hui Wang, and Xinghua Shi. 2020 a. Differential Privacy Protection Against Membership Inference Attack on Machine Learning for Genomic Data. In BIOCOMPUTING 2021: Proceedings of the Pacific Symposium. World Scientific, 26--37.

5. Ricky TQ Chen , Xuechen Li , Roger Grosse , and David Duvenaud . 2018 . Isolating Sources of Disentanglement in VAEs . In Proceedings of the 32nd International Conference on Neural Information Processing Systems. 2615--2625 . Ricky TQ Chen, Xuechen Li, Roger Grosse, and David Duvenaud. 2018. Isolating Sources of Disentanglement in VAEs. In Proceedings of the 32nd International Conference on Neural Information Processing Systems. 2615--2625.

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