CRII: OAC: Cyberinfrastructure for IoT Communications

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.