STTR Phase II:Blockchain-Enabled Machine Learning on Confidential Data

这项小型企业技术转让（STTR）第二阶段项目的更广泛影响/商业潜力将使敏感数据的协作成为可能。这将提高包括生物医学在内的敏感领域的公司和机构应用现有机器学习模型的能力，并提高他们训练新的、更复杂的模型的能力，从而提高对增强护理的洞察力。该项目将推进区块链技术，以维护机密数据的完整性，促进机器学习技术的共享开发。这个STTR第二阶段项目建议以保持数据机密性和确保可验证性的方式，推进在协调分散机器学习和分布式分类账领域的知识。该项目扩展了深度神经网络在需要数据隐私的领域的效用，例如涉及分散计算的伙伴关系或设置。该项目侧重于推理和联合模型训练。该技术方法将利用全同态加密（FHE）和安全多方计算（MPC）技术。这项工作将推进FHE密码系统，评估候选密码系统的实际性能，并开发由分布式账本介导的有效密码验证技术。除了在上述领域创建关键知识外，其他技术成果将包括可访问的开源工具，用于对加密数据进行神经网络推理，以及用于实际部署的模块化框架，包括与数据存储的接口。该工作计划将导致实现可验证的机密推断和联合模型训练的技术，并将在医学图像和非图像数据的现实世界分析中展示这些能力。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 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.