Differentially Private Deep Learning With Dynamic Privacy Budget Allocation and Adaptive Optimization-Reference-Cited by-同舟云学术

Differentially Private Deep Learning With Dynamic Privacy Budget Allocation and Adaptive Optimization

Published:2023 Issue: Volume:18 Page:4422-4435
ISSN:1556-6013
Container-title:IEEE Transactions on Information Forensics and Security
language:
Short-container-title:IEEE Trans.Inform.Forensic Secur.

Author:

Chen Lin¹^ORCID,Yue Danyang¹,Ding Xiaofeng¹^ORCID,Wang Zuan¹^ORCID,Choo Kim-Kwang Raymond²^ORCID,Jin Hai¹^ORCID

Affiliation:

1. National Engineering Research Center for Big Data Technology and System Laboratory, the Services Computing Technology and System Laboratory, and the Cluster and Grid Computing Laboratory, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China

2. Department of Information Systems and Cyber Security, The University of Texas at San Antonio, San Antonio, TX, USA

Funder

National Natural Science Foundation of China

Cloud Technology Endowed Professorship

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications,Safety, Risk, Reliability and Quality

Link

http://xplorestaging.ieee.org/ielx7/10206/9970396/10179278.pdf?arnumber=10179278

Reference49 articles.

1. Concentrated Differentially Private Gradient Descent with Adaptive per-Iteration Privacy Budget