Toward Few-step Adversarial Training from a Frequency Perspective
Authors: Hans Shih-Han Wang, Cory Cornelius, Brandon Edwards, Jason Martin | Published: 2020-10-13
S3ML: A Secure Serving System for Machine Learning Inference
Authors: Junming Ma, Chaofan Yu, Aihui Zhou, Bingzhe Wu, Xibin Wu, Xingyu Chen, Xiangqun Chen, Lei Wang, Donggang Cao | Published: 2020-10-13
COVID-19 Imaging Data Privacy by Federated Learning Design: A Theoretical Framework
Authors: Anwaar Ulhaq, Oliver Burmeister | Published: 2020-10-13
An Analysis of Robustness of Non-Lipschitz Networks
Authors: Maria-Florina Balcan, Avrim Blum, Dravyansh Sharma, Hongyang Zhang | Published: 2020-10-13 | Updated: 2023-04-18
To be Robust or to be Fair: Towards Fairness in Adversarial Training
Authors: Han Xu, Xiaorui Liu, Yaxin Li, Anil K. Jain, Jiliang Tang | Published: 2020-10-13 | Updated: 2021-05-18
TextHide: Tackling Data Privacy in Language Understanding Tasks
Authors: Yangsibo Huang, Zhao Song, Danqi Chen, Kai Li, Sanjeev Arora | Published: 2020-10-12
Differentially Private Secure Multi-Party Computation for Federated Learning in Financial Applications
Authors: David Byrd, Antigoni Polychroniadou | Published: 2020-10-12
Security and Privacy Considerations for Machine Learning Models Deployed in the Government and Public Sector (white paper)
Authors: Nader Sehatbakhsh, Ellie Daw, Onur Savas, Amin Hassanzadeh, Ian McCulloh | Published: 2020-10-12
ByzShield: An Efficient and Robust System for Distributed Training
Authors: Konstantinos Konstantinidis, Aditya Ramamoorthy | Published: 2020-10-10 | Updated: 2021-03-04
How Does Mixup Help With Robustness and Generalization?
Authors: Linjun Zhang, Zhun Deng, Kenji Kawaguchi, Amirata Ghorbani, James Zou | Published: 2020-10-09 | Updated: 2021-03-17