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

随着人工智能的发展，保护隐私的机器学习已经成为保护云应用中用户数据隐私的重要且有前途的技术。在现有的方法中，基于完全同态加密（FHE）的方法允许在不泄露原始数据信息的情况下对加密数据进行机器学习算法的计算。该项目解决了保护用户和模型提供者隐私的密文-密文FHE。本项目旨在通过算法-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。该项目为确保云计算和密码系统的信任根源提供了一个新的框架，以满足商业产品和国防的未来需求。该项目为基于密文-密文FHE的隐私保护神经网络推理开发了高效且可扩展的硬件架构。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-来加速神经网络的计算。研究重点包括：a)采用新颖的可重构和流水线框架，利用特殊素数进行快速模块化约简，为隐私保护神经网络设计高效的基础硬件模块，在模字长和多项式度上具有高可扩展性，即多项式乘法器；b)利用基于新型并行滤波技术的分而治之策略进一步提高多项式乘法器设计的效率；c)利用方案交换开发可重构和神经网络友好的FHE架构；d)通过方案切换设计一种高效的保护隐私的神经网络推理加速器，通过密文-密文操作保护用户和模型的隐私。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。

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.