Secure Multi-Party Learning and Inferring Insights Based on Encrypted Data

摘要

Respective sets of homomorphically encrypted training data are received from multiple users, each encrypted by a key of a respective user. The respective sets are provided to a combined machine learning model to determine corresponding locally learned outputs, each in an FHE domain of one of the users. Conversion is coordinated of the locally learned outputs in the FHE domains into an MFHE domain, where each converted locally learned output is encrypted by all of the users. The converted locally learned outputs are aggregated into a converted composite output in the MFHE domain. A conversion is coordinated of the converted composite output in the MFHE domain into the FHE domains of the corresponding users, where each converted decrypted composite output is encrypted by only a respective one of the users. The combined machine learning model is updated based on the converted composite outputs. The model may be used for inferencing.