  Privacy-preserving machine learning in data-sharing processes is an
ever-critical task that enables collaborative training of Machine Learning (ML)
models without the need to share the original data sources. It is especially
relevant when an organization must assure that sensitive data remains private
throughout the whole ML pipeline, i.e., training and inference phases. This
paper presents an innovative framework that uses Representation Learning via
autoencoders to generate privacy-preserving embedded data. Thus, organizations
can share the data representation to increase machine learning models'
performance in scenarios with more than one data source for a shared predictive
downstream task.
