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

这个小企业创新研究（SBIR）项目的更广泛的影响/商业潜力包括科学理解的进步和重大的社会和商业影响。在这个数据泄露似乎层出不穷的时代，该项目提供了一种应用机器学习力量的方法，同时又不会泄露敏感的原始数据。去中心化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的更复杂的问题上使用深度学习。此外，允许使用机密数据将使具有敏感数据的领域（如生物医学）的研究取得更快的进展。此外，去中心化计算提供了比现有计算基础设施（如云提供商）更低成本的承诺。这种更强大、更民主的力量将推动最先进技术的发展，并使更多人能够利用大规模机器学习。该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，推进协调分散安全机器学习领域的知识。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。虽然深度神经网络近年来取得了惊人的成果，但在保持数据机密性和确保可验证性的同时，在分散环境中实现训练模型的实用解决方案方面，进展有限。这是关键的挑战，预计该项目将产生解决方案。提议的方法包括定义一个协议，用于在不受信任的各方之间进行训练，该协议由分散的分类账调解，并涉及使用同态加密和计算验证技术。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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.