  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.
