Decentralized online social networks enhance users' privacy by empowering them to control their data. However, these networks mostly lack for practical solutions for building recommender systems in a privacy-preserving manner that help to improve the network’s services. Association rule mining is one of the basic building blocks for many recommender systems. In this paper, we propose an efficient approach enabling rule mining on distributed data. We leverage the Metropolis-Hasting random walk sampling and distributed FP-Growth mining algorithm to maintain the users' privacy. We evaluate our approach on three real-world datasets. Results reveal that the approach achieves high average precision scores (>96%) for as low as 1% sample size in well-connected social networks with remarkable reduction in communication and computational costs.