Substra: a framework for privacy-preserving, traceable and collaborative\n Machine Learning

Abstract

Machine learning is promising, but it often needs to process vast amounts of\nsensitive data which raises concerns about privacy. In this white-paper, we\nintroduce Substra, a distributed framework for privacy-preserving, traceable\nand collaborative Machine Learning. Substra gathers data providers and\nalgorithm designers into a network of nodes that can train models on demand but\nunder advanced permission regimes. To guarantee data privacy, Substra\nimplements distributed learning: the data never leave their nodes; only\nalgorithms, predictive models and non-sensitive metadata are exchanged on the\nnetwork. The computations are orchestrated by a Distributed Ledger Technology\nwhich guarantees traceability and authenticity of information without needing\nto trust a third party. Although originally developed for Healthcare\napplications, Substra is not data, algorithm or programming language specific.\nIt supports many types of computation plans including parallel computation plan\ncommonly used in Federated Learning. With appropriate guidelines, it can be\ndeployed for numerous Machine Learning use-cases with data or algorithm\nproviders where trust is limited.\n