CRII: OAC: Cyberinfrastructure for IoT Communications

美国受数据泄露和网络攻击影响的用户数量最近有所增加，使该国成为2023年最受攻击的目标。同时，物联网通信和机器学习算法的发展导致了需要分析的前所未有的数据涌入。这加强了在数据通信和机器学习训练期间确定有效保护交换信息的方法的紧迫性。完全同态加密是一种很有前途的解决方案，它对加密的数据进行操作而不进行解密。然而，它的实际集成面临着挑战，特别是在算法复杂性和计算限制方面，特别是在延迟方面。该项目旨在优化现有全同态加密方案中的资源密集型操作，并将优化算法无缝集成到联邦学习框架中。这将在保持学习性能的同时增强安全性，彻底改变物联网通信中的安全数据分析。通过使联邦学习用户和云提供商受益，该项目将增强远程医疗和无线通信等实际应用中的安全性，有助于增强关键部门的隐私、安全性和效率，最终推动科学造福社会。该项目开发了一种优化的全同态加密算法，以增强物联网（IoT）通信的安全性，并将优化的全同态加密算法集成到联邦学习框架中，以实现对加密数据的安全训练过程，同时保持学习性能。该项目包括以下目标：(1)通过优化资源密集型操作，开发低复杂度、完全同态的加密算法；(2)将优化后的全同态加密算法集成到联邦学习中，增强物联网通信的安全性；(3)利用图形处理单元上的并行处理，利用中央处理单元和图形处理单元的联合计算能力，加速完全同态加密和联邦学习。所提出的方法是推动未来安全物联网通信的关键一步。通过协同同态加密、并行处理和机器学习领域，该方法推进了该领域的理论基础，并引入了有助于其发展和演变的新方法。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 number of users affected by data breaches and cyberattacks in the United States has increased recently, making the country the most targeted in 2023. Concurrently, the evolution of the Internet of Things communications and machine learning algorithms has led to an unprecedented influx of data requiring analysis. This has intensified the urgency to identify efficient methods for safeguarding exchanged information during data communication and Machine-Learning training. Fully homomorphic encryption, operating on encrypted data without decryption, emerges as a promising solution. However, its practical integration faces challenges, notably in algorithm intricacy and computational constraints, especially regarding latency. This project aims to optimize resource-intensive operations in existing fully homomorphic encryption schemes and seamlessly integrate the optimized algorithm into federated learning frameworks. This will enhance security while preserving learning performance, revolutionizing secure data analysis in Internet of Things communications. By benefiting federated learning users and cloud providers, this project will enhance security in practical applications such as telehealth and wireless communications, contributing to enhanced privacy, security, and efficiency in critical sectors, ultimately advancing science for society.The project develops an optimized fully homomorphic encryption algorithm to enhance security in Internet of Things (IoT) communications and integrates the optimized fully homomorphic encryption algorithm into federated learning frameworks to enable a secure training process on the encrypted data while maintaining learning performance. The project encompasses the following objectives: (1) developing a low-complexity, fully homomorphic encryption algorithm by optimizing resource-intensive operations; (2) integrating the optimized fully homomorphic encryption algorithm into federated learning to bolster security in IoT communications; and (3) accelerating fully homomorphic encryption and federated learning using parallel processing on graphics processing units and harnessing the combined computational power of both central processing units and graphics processing units. The proposed methodology is a pivotal stride toward propelling secure IoT communications into the future. By synergizing the domains of homomorphic encryption, parallel processing, and machine learning, this approach advances the field’s theoretical underpinnings and introduces novel methodologies that contribute to its growth and evolution.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.