Efficient Sparse Least Absolute Deviation Regression With Differential Privacy-Reference-Cited by-同舟云学术

Efficient Sparse Least Absolute Deviation Regression With Differential Privacy

Published:2024 Issue: Volume:19 Page:2328-2339
ISSN:1556-6013
Container-title:IEEE Transactions on Information Forensics and Security
language:
Short-container-title:IEEE Trans.Inform.Forensic Secur.

Author:

Liu Weidong¹^ORCID,Mao Xiaojun²^ORCID,Zhang Xiaofei³^ORCID,Zhang Xin⁴^ORCID

Affiliation:

1. School of Mathematical Sciences, MoE Key Laboratory of Artificial Intelligence, Shanghai Jiao Tong University, Shanghai, China

2. School of Mathematical Sciences, Ministry of Education Key Laboratory of Scientific and Engineering Computing, Shanghai Jiao Tong University, Shanghai, China

3. School of Statistics and Mathematics, Zhongnan University of Economics and Law, Wuhan, China

4. Department of Statistics, Iowa State University, Ames, IA, USA

Funder

NSFC

Shanghai Rising-Star Program

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/10206/10319981/10379016.pdf?arnumber=10379016

Reference52 articles.

1. The Algorithmic Foundations of Differential Privacy

2. On Sparse Linear Regression in the Local Differential Privacy Model

3. The cost of privacy: Optimal rates of convergence for parameter estimation with differential privacy

4. High dimensional sparse linear regression under local differential privacy: Power and limitations;Wang

5. DPLK-Means: A Novel Differential Privacy K-Means Mechanism

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