Fully homomorphic encryption (FHE) is a technique that enables statistical processing and machine learning while protecting data including sensitive information collected by such single board computers (SBCs) on a cloud server. Among FHE schemes, the TFHE scheme is capable of homomorphic NAND operation, and unlike other FHE schemes, it can perform any operation, such as minimum, maximum, and comparison operations. However, TFHE requires Torus Learning With Error (TLWE) encryption, which encrypts one bit at a time, resulting in less efficient encryption and larger ciphertext size than the other schemes. In addition, SBCs have a limited number of hardware accelerators compared to servers, making it difficult to perform the same optimization as servers. In this study, we propose a novel SBC-specific design TFHE-SBC to accelerate the client-side TFHE operations and achieve communication and energy efficiency. Experimental results show that the TFHE-SBC encryption is up to 2486 times faster, communication efficiency improves 512 times higher, and 12 to 2004 times more energy efficiency than the state-of-the-art.

翻译：全同态加密（FHE）是一种能够在保护数据（包括由单板计算机收集的敏感信息）的前提下，在云服务器上进行统计处理和机器学习的技术。在众多FHE方案中，TFHE方案支持同态NAND运算，且与其他FHE方案不同，它能够执行任意运算，例如最小值、最大值及比较运算。然而，TFHE需要采用环面误差学习（TLWE）加密，该加密方式每次仅加密一位数据，导致其加密效率低于其他方案且密文尺寸更大。此外，与服务器相比，单板计算机的硬件加速器数量有限，难以实现与服务器相同的优化效果。本研究提出了一种专为单板计算机设计的新型方案TFHE-SBC，旨在加速客户端TFHE运算并实现通信与能效优化。实验结果表明，相较于现有最优方案，TFHE-SBC的加密速度最高提升2486倍，通信效率提升512倍，能效提升12至2004倍。