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

这一小型企业创新研究(SBIR)项目的广泛影响/商业潜力包括在科学认识方面的进步以及重大的社会和商业影响。在一个数据泄露似乎永无止境的时代，该项目提供了一种应用机器学习的力量，同时永远不会泄露敏感原始数据的方法。分散计算可以增加可以训练的模型的规模，这将允许在一系列领域中对更复杂的问题使用深度学习。此外，允许使用机密数据将允许在生物医学等具有敏感数据的领域取得更快的研究进展。此外，分散计算提供了比现有计算基础设施(如云提供商)更低的成本承诺。这种更大、更民主的权力将打破最先进的界限，也使更多的人能够利用大规模机器学习。这个SBIR第一阶段项目建议以保持数据机密性和确保可验证性的方式，促进分散的安全机器学习与区块链协调领域的知识。研发还将促进对零知识、计算验证和同态神经网络的理解和实用性。尽管深度神经网络近年来取得了惊人的成果，但在实现在既保持数据机密性又确保可验证性的情况下，在非集中化背景下训练模型的实际解决办法方面取得的进展有限。这是一个关键的挑战，预计这个项目将产生一个解决方案。建议的方法包括定义一个在不受信任的各方之间进行培训的协议，该协议由一个分散的分类账进行调解，并涉及使用同态加密和计算验证技术。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.