In this paper, we study a distributed privacy-preserving learning problem in social networks with general topology. The agents can communicate with each other over the network, which may result in privacy disclosure, since the trustworthiness of the agents cannot be guaranteed. Given a set of options which yield unknown stochastic rewards, each agent is required to learn the best one, aiming at maximizing the resulting expected average cumulative reward. To serve the above goal, we propose a four-staged distributed algorithm which efficiently exploits the collaboration among the agents while preserving the local privacy for each of them. In particular, our algorithm proceeds iteratively, and in every round, each agent i) randomly perturbs its adoption for the privacy-preserving purpose, ii) disseminates the perturbed adoption over the social network in a nearly uniform manner through random walking, iii) selects an option by referring to the perturbed suggestions received from its peers, and iv) decides whether or not to adopt the selected option as preference according to its latest reward feedback. Through solid theoretical analysis, we quantify the trade-off among the number of agents (or communication overhead), privacy preserving and learning utility. We also perform extensive simulations to verify the efficacy of our proposed social learning algorithm.

翻译：本文研究了一般拓扑结构社交网络中的分布式隐私保护学习问题。由于智能体间的可信性无法保证，网络中的通信可能导致隐私泄露。给定一组产生未知随机奖励的选项，每个智能体需学习选择最优选项，以最大化期望平均累积奖励。为此，我们提出一种四阶段分布式算法，该算法在高效利用智能体间协作的同时，保护每个智能体的本地隐私。具体而言，算法迭代进行，每轮中每个智能体：(i) 随机扰动其采纳结果以实现隐私保护；(ii) 通过随机游走将扰动后的采纳结果以近似均匀的方式分发至社交网络；(iii) 根据从同伴处接收的扰动建议选择选项；(iv) 根据最新奖励反馈决定是否将所选选项作为偏好采纳。通过严格的理论分析，我们量化了智能体数量（或通信开销）、隐私保护与学习效用之间的权衡关系。大量仿真实验验证了所提社交学习算法的有效性。