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Training deep neural networks often forces users to work in a distributed or outsourced setting, accompanied with privacy concerns.
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PyTorch: An Imperative Style, High-Performance Deep Learning Library
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Reducing leakage in distributed deep learning for sensitive health data
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Updates-leak: Data set inference and reconstruction attacks in online learning. In 29th { \{ USENIX } \} Security Symposium ( { \{ USENIX } \} Security 20) . 1291–1308
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Diederik P. Kingma and Jimmy Ba. 2017 · 2017
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Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
Han Xiao, Kashif Rasul, and Roland Vollgraf. 2017 · 2017
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Model inversion attacks against collaborative inference. In Proceedings of the 35th Annual Computer Security Applications Conference . ACM, San Juan Puerto Rico, 148–162
Zecheng He, Tianwei Zhang, and Ruby B. Lee. 2019 · 2019
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Deep leakage from gradients
Ligeng Zhu and Song Han. 2020 · 2020
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Gradient Inversion Attack: Leaking Private Labels in Two-Party Split Learning
Sanjay Kariyappa and Moinuddin K Qureshi. 2021 · 2021
Closest in time.
Label Leakage and Protection in Two-party Split Learning
Oscar Li, Jiankai Sun, Xin Yang, Weihao Gao, Hongyi Zhang, Junyuan Xie, Virginia Smith, and Chong Wang. 2021 · 2021
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Unleashing the tiger: Inference attacks on split learning
Dario Pasquini, Giuseppe Ateniese, and Massimo Bernaschi. 2021 · 2021
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Clustering Label Inference Attack against Practical Split Learning
Junlin Liu and Xinchen Lyu. 2022 · 2022
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