2 papers
cs.CR2022
Scalable Multi-Party Privacy-Preserving Gradient Tree Boosting over Vertically Partitioned Dataset with Outsourced Computations
Kennedy Edemacu, Beakcheol Jang, Jong Wook Kim
Due to privacy concerns, multi-party gradient tree boosting algorithms have become widely popular amongst machine learning researchers and practitioners. However, limited existing…
cs.LG2021
Reliability Check via Weight Similarity in Privacy-Preserving Multi-Party Machine Learning
Kennedy Edemacu, Beakcheol Jang, Jong Wook Kim
Multi-party machine learning is a paradigm in which multiple participants collaboratively train a machine learning model to achieve a common learning objective without sharing thei…