Exploring Machine Learning Privacy/Utility Trade-Off from a Hyperparameters Lens
Author:
Affiliation:
1. New York University Abu Dhabi (NYUAD),eBrain Lab, Division of Engineering,Abu Dhabi,United Arab Emirates
2. Queen's University,IEMN CNRS-8520, INSA Hauts-de-Franc,Belfast,UK
Funder
NYUAD Center for CyberSecurity (CCS)
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10190990/10190992/10191743.pdf?arnumber=10191743
Reference35 articles.
1. On the convergence and calibration of deep learning with differential privacy;bu;ArXiv Preprint,2021
2. Efficient hyperparameter optimization for differentially private deep learning;priyanshu;ArXiv Preprint,2021
3. Deep Learning with Differential Privacy
4. Differentially Private Neural Networks with Bounded Activation Function
5. On the convergence and calibration of deep learning with differential privacy;bu;ArXiv Preprint,2021
Cited by 2 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. Robust and privacy-preserving collaborative training: a comprehensive survey;Artificial Intelligence Review;2024-06-20
2. Differentially-Private Neural Network Training with Private Features and Public Labels;Lecture Notes in Computer Science;2024
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