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In this paper, we study a distributed privacy-preserving learning problem insocial networks with general topology. The agents can communicate with eachother over the network, which may result in privacy disclosure, since thetrustworthiness of the agents cannot be guaranteed. Given a set of optionswhich yield unknown stochastic rewards, each agent is required to learn thebest one, aiming at maximizing the resulting expected average cumulativereward. To serve the above goal, we propose a four-staged distributed algorithmwhich efficiently exploits the collaboration among the agents while preservingthe local privacy for each of them. In particular, our algorithm proceedsiteratively, and in every round, each agent i) randomly perturbs its adoptionfor the privacy-preserving purpose, ii) disseminates the perturbed adoptionover the social network in a nearly uniform manner through random walking, iii)selects an option by referring to the perturbed suggestions received from itspeers, and iv) decides whether or not to adopt the selected option aspreference according to its latest reward feedback. Through solid theoreticalanalysis, we quantify the trade-off among the number of agents (orcommunication overhead), privacy preserving and learning utility. We alsoperform extensive simulations to verify the efficacy of our proposed sociallearning algorithm.

A Distributed Privacy-Preserving Learning Dynamics in General Social Networks