A Scalable Approach for Privacy-Preserving Collaborative Machine Learning

Machine learning applications can achieve significant performance gains by training on large volumes of data. In many applications, the training data is distributed across multiple data-owners, such as patient records at multiple medical institutions, and furthermore contains sensitive information, e.g., genetic information, financial transactions, and geolocation information. Such settings give rise to the following key problem that is the focus of this paper: How can multiple data-owners jointly train a machine learning model while keeping their individual datasets private from the other parties? More specifically, we consider a distributed learning scenario in which N data-owners (clients) wish to train a logistic regression model jointly without revealing information about their individual datasets to the other parties, even if up to T out of N clients collude. Our focus is on the semi-honest adversary setup, where the corrupted parties follow the protocol but may leak information in an attempt to learn the training dataset.