Collaborative and Confidential Junction Trees for Hybrid Bayesian Networks
Roberto Gheda, Abele Mălan, Thiago Guzella, Carlo Lancia, Robert Birke, Lydia Chen
Bayesian Network models are a powerful tool to collaboratively optimize production processes in various manufacturing industries.While interacting, collaborating parties must preserve their business secrets by maintaining the confidentiality of their model structures and parameters.While most realistic industry scenarios involve hybrid scenarios, handling both discrete and continuous data, current state-of-the-art methods for collaborative and confidential inference only support discrete data and have high communication costs.In a centralized setting, Junction Trees enable efficient inference even in hybrid scenarios without discretizing continuous variables, but no extension for collaborative and confidential scenarios exists.To address this research gap, we introduce Hybrid CCJT, the first framework for confidential multiparty inference in hybrid domains, comprising:(i) a method to construct a strongly-rooted Junction Tree across collaborating parties through a novel construct of interface cliques; and,(ii) a protocol for confidential inference built upon multiparty computation primitives comprising a one-time alignment phase and a belief propagation system for combining the inference results across the Junction Tree cliques.Extensive evaluation on nine datasets shows that Hybrid CCJT improves the predictive accuracy of continuous target variables by 32% on average against the state-of-the-art, while reducing communication costs up to 331X under purely discrete scenarios.