The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase II project will enable collaboration with sensitive data. This will increase the ability of companies and institutions in sensitive domains, including biomedicine, to apply existing machine learning models and increase their ability to train new, more sophisticated models, thereby improving insights for enhanced care. This project will advance a blockchain technique to maintain the integrity of confidential data for shared development of machine learning techniques.
This STTR Phase II project proposes to advance knowledge in the area of coordinating decentralized machine learning with a distributed ledger in a manner that maintains data confidentiality and ensures verifiability. This project extends the utility of deep neural networks in domains requiring data privacy, such as partnerships or settings involving decentralized computation. The project focuses on both inference and joint model training. The technical approach will leverage techniques from fully homomorphic encryption (FHE) and secure multiparty computation (MPC). The work will advance an FHE cryptosystem, assess practical performance of candidate cryptosystems, and develop efficient cryptographic verification techniques mediated by the distributed ledger. In addition to creating critical knowledge in the preceding areas, other technical results will include accessible open-source tooling for neural network inference on encrypted data and a modular framework for practical deployment, including interfacing with data stores. The program of work will lead to technology that enables verifiable confidential inference and joint model training and will demonstrate these capabilities on real-world analyses in medicine with medical image and non-image data.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
