  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.
