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Privacy-Preserving Push-Sum Average Consensus via State Decomposition by Xiaomeng Chen; Lingying Huang; Kemi Ding; Subhrakanti Dey; Ling Shi is a Engineering article available to read on EtoBox.

What is Privacy-Preserving Push-Sum Average Consensus via State Decomposition about?

Average consensus is extensively used in distributed networks for computation and control, where all the agents constantly communicate with each other and update their states in order to reach an agreement. Under a general average consensus algorithm, information exchanged through wireless or wired communication networks could lead to the disclosure of sensitive and private information. In this paper, we propose a privacy-preserving push-sum approach for directed networks that can protect the privacy of all agents while achieving average consensus simultaneously. Each node decomposes its initial state arbitrarily into two substates, and their average equals to the initial state, guaranteeing that the agent's state will converge to the accurate average consensus. Only one substate is exchanged by the node with its neighbours over time, and the other one is reserved. That is to say, only the exchanged substate would be visible to an adversary, preventing the initial state information from leakage. Different from the existing state-decomposition approach which only applies to undirected graphs, our proposed approach is applicable to strongly connected digraphs. In addition, in direct

Who reads Privacy-Preserving Push-Sum Average Consensus via State Decomposition?

Author

Xiaomeng Chen; Lingying Huang; Kemi Ding; Subhrakanti Dey; Ling Shi

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Published

2023

Language

EN

Field

Engineering (Physical Sciences)