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arXiv · 2004.01312

Preserving Statistical Privacy in Distributed Optimization

Nirupam Gupta·Shripad Gade·Nikhil Chopra·Nitin H. Vaidya
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Abstract

We present a distributed optimization protocol that preserves statistical privacy of agents' local cost functions against a passive adversary that corrupts some agents in the network. The protocol is a composition of a distributed ``{\em zero-sum}" obfuscation protocol that obfuscates the agents' local cost functions, and a standard non-private distributed optimization method. We show that our protocol protects the statistical privacy of the agents' local cost functions against a passive adversary that corrupts up to $t$ arbitrary agents as long as the communication network has $(t+1)$-vertex connectivity. The ``{\em zero-sum}" obfuscation protocol preserves the sum of the agents' local cost functions and therefore ensures accuracy of the computed solution.

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Nirupam Gupta, Shripad Gade, Nikhil Chopra, Nitin H. Vaidya. 2020-04-03. Preserving Statistical Privacy in Distributed Optimization. https://arxiv.org/abs/2004.01312

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