SBIR Phase I: Blockchain-Enabled Machine Learning on Confidential Data

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) project includes advances in scientific understanding and substantial societal and commercial impacts. In an era with seemingly endless data breaches, the project offers a way of applying the power of machine learning while never disclosing sensitive raw data. Decentralized computation can increase the scale of models that may be trained, which will allow the use of deep learning on more complicated problems across a range of fields. Additionally, allowing confidential data to be used will allow more rapid research advances in fields with sensitive data, such as biomedicine. Furthermore, decentralized computation offers the promise of lower cost than existing computational infrastructures such as cloud providers. This greater, and more democratic, power will push the boundaries of the state-of-the-art and also enable more people to leverage large-scale machine learning. This SBIR Phase I project proposes to advance knowledge in the area of coordinating decentralized secure machine learning with a blockchain in a manner that maintains data confidentiality and ensures verifiability. The R&D will also advance understanding and practicality of zero knowledge computational verification and homomorphic neural networks. While deep neural networks have yielded astounding results in recent years, there has been limited progress towards achieving a practical solution to training models in a decentralized context while both maintaining data confidentiality and ensuring verifiability. This is the key challenge and it is anticipated that this project will yield a solution. The proposed approach involves defining a protocol for training amongst untrusted parties that is mediated by a decentralized ledger and involves the use of homomorphic encryption and a computational verification technique. 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.