LAUSR

We propose a decentralized optimization algorithm that preserves the privacy of agents’ cost functions without sacrificing accuracy, termed EFPSN. The algorithm adopts Paillier cryptosystem to construct zero-sum functional perturbations. Then, based on the perturbed cost functions, any existing decentralized optimization algorithm can be utilized to obtain the accurate solution. We theoretically prove that EFPSN is $(\epsilon, \delta)$ -differentially private and can achieve infinitesimally small $\epsilon,\delta $ under deliberate parameter settings. Numerical experiments further confirm the effectiveness of the algorithm.