Multi-party Secure Broad Learning System for Privacy Preserving. (arXiv:2206.11133v1 [cs.CR])

Multi-party learning is an indispensable technique for improving the learning
performance via integrating data from multiple parties. Unfortunately, directly
integrating multi-party data would not meet the privacy preserving
requirements. Therefore, Privacy-Preserving Machine Learning (PPML) becomes a
key research task in multi-party learning. In this paper, we present a new PPML
method based on secure multi-party interactive protocol, namely Multi-party
Secure Broad Learning System (MSBLS), and derive security analysis of the
method. The existing PPML methods generally cannot simultaneously meet multiple …

party privacy system