User-Centric Federated Matrix Factorization Based on Differential Privacy-Reference-Cited by-同舟云学术

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User-Centric Federated Matrix Factorization Based on Differential Privacy

Published:2023-05 Issue:3 Volume:27 Page:21-27
ISSN:1089-7801
Container-title:IEEE Internet Computing
language:
Short-container-title:IEEE Internet Comput.

Author:

Liu Yang¹^ORCID,Xu Wanyin²^ORCID,Lai Jiaxin³^ORCID,Wang Jiabo³^ORCID

Affiliation:

1. Peng Cheng Laboratory,, Harbin Institute of Technology, Shenzhen, China

2. Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies,, Harbin Institute of Technology, Shenzhen, China

3. Harbin Institute of Technology, Shenzhen, China

Funder

Shenzhen Basic Research General Project

Shenzhen Stable Supporting Program General Project

Shenzhen Basic Research Key Project

Peng Cheng Laboratory Project

Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Computer Networks and Communications

Link

http://xplorestaging.ieee.org/ielx7/4236/10122637/10090858.pdf?arnumber=10090858

Reference14 articles.

1. Deep Learning with Differential Privacy

2. Collaborative Filtering Recommendation Algorithm Based on Improved BiasSVD

3. RR-LADP: A Privacy-Enhanced Federated Learning Scheme for Internet of Everything

4. Local Differential Privacy for Deep Learning

5. The Algorithmic Foundations of Differential Privacy

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1. Connected Living—Part I;IEEE Internet Computing;2023-05

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