Privacy of Agents' Costs in Peer-to-Peer Distributed Optimization
It addresses the problem of privacy preservation in distributed optimization for multi-agent systems, but the result is incremental as it builds on existing privacy mechanisms and requires strong connectivity assumptions.
This paper proposes a protocol for peer-to-peer distributed optimization that preserves statistical privacy of agents' cost functions against a passive adversary, guaranteeing privacy of affine cost parts if corrupted agents do not form a vertex cut (i.e., network has (t+1)-connectivity for t corrupted agents).
In this paper, we propose a protocol that preserves (statistical) privacy of agents' costs in peer-to-peer distributed optimization against a passive adversary that corrupts certain number of agents in the network. The proposed protocol guarantees privacy of the affine parts of the honest agents' costs (agents that are not corrupted by the adversary) if the corrupted agents do not form a vertex cut of the underlying communication topology. Therefore, if the (passive) adversary corrupts at most t arbitrary agents in the network then the proposed protocol can preserve the privacy of the affine parts of the remaining honest agents' costs if the communication topology has (t+1)-connectivity. The proposed privacy protocol is a composition of a privacy mechanism (we propose) with any (non-private) distributed optimization algorithm.