Privacy-Preserving Student Learning with Differentially Private Data-Free Distillation-Reference-Cited by-同舟云学术

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Privacy-Preserving Student Learning with Differentially Private Data-Free Distillation

Published:2022-09-26 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE 24th International Workshop on Multimedia Signal Processing (MMSP)
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Author:

Liu Bochao¹,Lu Jianghu¹,Wang Pengju¹,Zhang Junjie²,Zeng Dan²,Qian Zhenxing³,Ge Shiming¹

Affiliation:

1. Institute of Information Engineering, Chinese Academy of Sciences,Beijing,China,100095

2. School of Communication and Information Engineering, Shanghai University,Shanghai,China,200444

3. School of Computer Science, Fudan University,Shanghai,China,200433

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9948698/9948704/09950001.pdf?arnumber=9950001

Reference28 articles.

1. Deep Learning with Differential Privacy

2. Deep learning with label differential privacy;ghazi;NeurIPS,2021

3. Antipodes of label differential privacy: Pate and alibi;malek esmaeili;NeurIPS,2021

4. Label private deep learning training based on secure multiparty computation and differential privacy;yuan;NeurIPS workshop,0

5. Label differential privacy via clustering;esfandiari;AISTATS,2022

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Towards Independent Students’ Activities, Online Environment and Learning Performance: An Investigation through Synthetic Data and Artificial Neural Networks;Informatics;2023-04-10

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