CCBNet: Confidential Collaborative Bayesian Networks Inference

TL;DR

CCBNet is a novel framework enabling confidential collaborative inference on Bayesian networks, allowing multiple parties to jointly analyze data without revealing sensitive information, and is scalable to large industrial applications.

Contribution

This paper introduces CCBNet, the first framework for privacy-preserving collaborative Bayesian network inference using secret sharing, scalable to large multi-party industrial scenarios.

Findings

Achieves predictive quality comparable to centralized methods.

Scales to 16-128 parties with large networks.

Reduces computational overhead by 23% on average.

Abstract

Effective large-scale process optimization in manufacturing industries requires close cooperation between different human expert parties who encode their knowledge of related domains as Bayesian network models. For instance, Bayesian networks for domains such as lithography equipment, processes, and auxiliary tools must be conjointly used to effectively identify process optimizations in the semiconductor industry. However, business confidentiality across domains hinders such collaboration, and encourages alternatives to centralized inference. We propose CCBNet, the first Confidentiality-preserving Collaborative Bayesian Network inference framework. CCBNet leverages secret sharing to securely perform analysis on the combined knowledge of party models by joining two novel subprotocols: (i) CABN, which augments probability distributions for features across parties by modeling them into secret shares of their normalized combination; and (ii) SAVE, which aggregates party inference result shares through distributed variable elimination. We extensively evaluate CCBNet via 9 public Bayesian networks. Our results show that CCBNet achieves predictive quality that is similar to the ones of centralized methods while preserving model confidentiality. We further demonstrate that CCBNet scales to challenging manufacturing use cases that involve 16-128 parties in large networks of 223-1003 features, and decreases, on average, computational overhead by 23%, while communicating 71k values per request. Finally, we showcase possible attacks and mitigations for partially reconstructing party networks in the two subprotocols.