Collaborative Research: SHF: Small: Efficient and Scalable Privacy-Preserving Neural Network Inference based on Ciphertext-Ciphertext Fully Homomorphic Encryption

沿着人工智能的发展，隐私保护机器学习已经成为一种重要且有前途的技术，用于保护云应用程序中的用户数据隐私。在现有的方法中，基于全同态加密（FHE）的方法允许在加密数据上计算机器学习算法，而不会泄露原始数据信息。该项目致力于保护用户和模型提供者隐私的密文-密文FHE。该项目旨在通过算法-硬件协同优化，将基于密文-密文FHE的神经网络推理的硬件效率提高几个数量级。本项目提出了一种新的框架，用于确保云计算和密码系统的信任根，以满足未来商业产品和国防的需求。本项目开发了基于密文-密文FHE的隐私保护神经网络推理的高效和可扩展的硬件架构。该项目利用方案切换-使用基于算术的线性函数方案和基于布尔逻辑的非线性函数方案-来加速神经网络计算。研究重点包括：a）为隐私保护神经网络设计在模的字长和多项式次数上具有高可扩展性的有效基本硬件构建块，即，B）采用基于并行滤波技术的分治策略，进一步提高多项式乘法器的设计效率; c）采用方案切换技术，开发一种可重构的神经网络友好的FHE结构;和d）设计一种高效的带有密文的隐私保护神经网络推理加速器-该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的学术价值和更广泛的影响评审标准。

Along with the evolution of artificial intelligence, privacy-preserving machine learning has emerged as an important and promising technique for protecting user-data privacy in cloud applications. Among the existing approaches, fully homomorphic encryption (FHE) based methods allow machine learning algorithms to be computed on encrypted data, while no original data information is leaked. This project addresses ciphertext-ciphertext FHE that preserves the privacy of both the user and model providers. This project aims to improve the hardware efficiency of ciphertext-ciphertext FHE-based neural network inference by orders of magnitude through algorithm-hardware co-optimization. This project yields a novel framework for ensuring the root of trust in cloud computing and cryptosystems to meet the future needs of both commercial products and national defense.This project develops efficient and scalable hardware architectures for privacy-preserving neural network inference based on ciphertext-ciphertext FHE. This project leverages scheme switching - using arithmetic-based schemes for linear functions and Boolean logic-based schemes for non-linear functions - to accelerate the neural network computations. Research thrusts include: a) Designing efficient fundamental hardware building blocks with high scalability over word-length of modulus and degree of polynomial for privacy-preserving neural network, i.e., polynomial multipliers, by employing novel reconfigurable and pipelining framework and exploiting special primes to perform fast modular reduction; b) Further improving the efficiency of polynomial multiplier designs by utilizing a divide and conquer strategy based on a novel parallel filter technique; c) Developing a reconfigurable and neural network friendly FHE architecture using scheme switching; and d) Designing an efficient accelerator of privacy-preserving neural network inference with ciphertext-ciphertext operations via scheme switching that protects the privacy of both the user and model.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.