preserving statistical privacy in distributed optimization

• DOI: 10.48550/arxiv.2004.01312
• Publication Date: 2021-07-01
• Authors: Nirupam Gupta; Shripad Gade; Nikhil Chopra; Nitin H. Vaidya
• Keywords: FOS: Computer and information sciences; 0209 industrial biotechnology; Computer Science - Cryptography and Security; Computer Science - Distributed, Parallel, and Cluster Computing; Distributed, Parallel, and Cluster Computing (cs.DC); 02 engineering and technology; Cryptography and Security (cs.CR)

The updated version has simpler proofs. The paper has been peer-reviewed, and accepted for the IEEE Control Systems Letters (L-CSS 2021)<br/>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.<br/>