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Local Graph-homomorphic Processing for Privatized Distributed Systems. (arXiv:2210.15414v1 [cs.CR])
Oct. 28, 2022, 1:24 a.m. | Elsa Rizk, Stefan Vlaski, Ali H. Sayed
cs.CR updates on arXiv.org arxiv.org
We study the generation of dependent random numbers in a distributed fashion
in order to enable privatized distributed learning by networked agents. We
propose a method that we refer to as local graph-homomorphic processing; it
relies on the construction of particular noises over the edges to ensure a
certain level of differential privacy. We show that the added noise does not
affect the performance of the learned model. This is a significant improvement
to previous works on differential privacy for …
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