Privacy-preserving Decentralized Optimization via Decomposition release_eunv7tsyave67hikuzkuyu7hxi

by Chunlei Zhang, Huan Gao, Yongqiang Wang

Released as a article .

2018  

Abstract

This paper considers the problem of privacy-preservation in decentralized optimization, in which N agents cooperatively minimize a global objective function that is the sum of N local objective functions. We assume that each local objective function is private and only known to an individual agent. To cooperatively solve the problem, most existing decentralized optimization approaches require participating agents to exchange and disclose estimates to neighboring agents. However, this results in leakage of private information about local objective functions, which is undesirable when adversaries exist and try to steal information from participating agents. To address this issue, we propose a privacy-preserving decentralized optimization approach based on proximal Jacobian ADMM via function decomposition. Numerical simulations confirm the effectiveness of the proposed approach.
In text/plain format

Archived Files and Locations

application/pdf  374.1 kB
file_yga5oci6qvgf3ejuk6udzwwyfe
arxiv.org (repository)
web.archive.org (webarchive)
Read Archived PDF
Preserved and Accessible
Type  article
Stage   submitted
Date   2018-08-28
Version   v1
Language   en ?
arXiv  1808.09566v1
Work Entity
access all versions, variants, and formats of this works (eg, pre-prints)
Catalog Record
Revision: 75c478c3-371f-41cd-b496-553a2adf8390
API URL: JSON