Private and Accurate Decentralized Optimization via Encrypted and Structured Functional Perturbation
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DOI: 10.1109/lcsys.2022.3232210
Abstract: 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… read more here.
Keywords: private accurate; tex math; inline formula; decentralized optimization ... See more keywords