Confidentiality-Preserving Collaborative Bayesian Networks

Confidentiality-Preserving Collaborative Bayesian Networks

Mălan, Abel (TU Delft Electrical Engineering, Mathematics and Computer Science; TU Delft Data-Intensive Systems)

Chen, Lydia Y. (mentor)
Decouchant, Jérémie (mentor)
Guzella, Thiago (mentor)
Kulahcioglu Ozkan, Burcu (graduation committee)

Delft University of Technology

Computer Science

2023-08-24

Effective large-scale process optimization in manufacturing industries requires close cooperation between different parties of human experts who encode their knowledge of related domains as Bayesian network models. For example, parties in the steel industry must collaboratively use their Bayesian networks on process parameters at the maker, steel properties, and application demands at the client to identify process optimizations effectively. However, business confidentiality across domains hinders collaboration, demanding 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: CABN, which augments probability distributions for features across modeling parties into secret shares of their normalized combination; and (ii) SAVE, which aggregate party inference result shares through distributed variable elimination. We extensively evaluate CCBNet on nine public Bayesian networks. Our results show CCBNet achieves similar predictive quality to centralized methods while preserving model confidentiality. We finally demonstrate that CCBNet scales to challenging manufacturing use cases, where involving many (16-128) parties in large networks (223-1003 features), on average, enables 45% less computation while communicating 251k values/request.

Bayesian Network
Secure Multi-Party Computation
Collaboration

http://resolver.tudelft.nl/uuid:192a90ed-f6fc-4d5b-b3bc-7cf9b67e6754

2024-01-24

Student theses

master thesis

© 2023 Abel Mălan