Local Graph-homomorphic Processing for Privatized Distributed Systems

• URL: http://arxiv.org/abs/2210.15414v1
• Date: Wed, 26 Oct 2022 10:00:14 GMT
• Title: Local Graph-homomorphic Processing for Privatized Distributed Systems
• Authors: Elsa Rizk, Stefan Vlaski, Ali H. Sayed
• Abstract summary: 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 distributed algorithms.
• Score: 57.14673504239551
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 distributed algorithms, where the noise was added in a less structured manner without respecting the graph topology and has often led to performance deterioration. We illustrate the theoretical results by considering a linear regression problem over a network of agents.

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