A secure collaborative machine learning framework based on data locality

Advancements in big data analysis offer cost-effective opportunities to improve decision-making in numerous areas such as health care, economic productivity, crime, and resource management. Nowadays, data holders are tending to sharing their data for better outcomes from their aggregated data. However, the current tools and technologies developed to manage big data are often not designed to incorporate adequate security or privacy measures during data sharing. In this paper, we consider a scenario where multiple data holders intend to find predictive models from their joint data without revealing their own data to each other. Data locality property is used as an alternative to multi-party computation (SMC) techniques. Specifically, we distribute the centralized learning task to each data holder as local learning tasks in a way that local learning is only related to local data. Along with that, we propose an efficient and secure protocol to reassemble local results to get the final result. Correctness of our scheme is proved theoretically and numerically. Security analysis is conducted from the aspect of information theory.