SafeNet: The Unreasonable Effectiveness of Ensembles in Private Collaborative Learning-Reference-Cited by-同舟云学术

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SafeNet: The Unreasonable Effectiveness of Ensembles in Private Collaborative Learning

Published:2023-02 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML)
language:
Short-container-title:

Author:

Chaudhari Harsh¹,Jagielski Matthew²,Oprea Alina¹

Affiliation:

1. Northeastern University

2. Google Research

Funder

U.S. Army Combat Capabilities Development Command Army Research Laboratory

Department of Defense Multidisciplinary Research Program of the University Research Initiative (MURI)

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10135546/10136129/10136155.pdf?arnumber=10136155

Reference103 articles.

1. Stronger data poisoning attacks break data sanitization defenses

2. MP-SPDZ: A Versatile Framework for Multi-Party Computation

3. Do Better ImageNet Models Transfer Better?

4. Understanding black-box predictions via influence functions;koh;ICML,2017

5. Intrinsic Certified Robustness of Bagging against Data Poisoning Attacks

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