Privacy concerns often prevent different parties from sharing their data in order to carry out data mining applications on their joint data. Privacy-preserving data mining seeks to address this by enabling parties to jointly compute a data mining algorithm on distributed data without sharing their data. In this paper, we address a particular data mining problem, that of learning the parameters of Bayesian network on a vertically partitioned database. We provide a simple privacy-preserving protocol for learning the parameters of Bayesian network on vertically partitioned databases. In comparison to the previously known solution for this problem (Meng, Sivakumar, and Kargupta, 2004), our solution provides better performance, full privacy, and complete accuracy. In combination with our previous work on privacy-preserving learning of Bayesian network structure on vertically partitioned databases, this work provides a complete privacy-preserving protocol for learning Bayesian networks (both structure and parameters) on vertically partitioned data, with very little over-head beyond computing the structure alone.