论文标题
memfhe：内存中具有完全同型加密的端到端计算
MemFHE: End-to-End Computing with Fully Homomorphic Encryption in Memory
论文作者
论文摘要
数据量的增加和问题的复杂性增长导致对云计算的不断增长。但是，许多应用程序，最著名的是在医疗保健，金融或国防，要求当今解决方案无法完全解决的安全和隐私。完全同态加密（FHE）通过在处理过程中添加数据的机密性来提高当今解决方案的标准。它允许在无需解密的情况下对完全加密的数据进行计算，从而完全保留隐私。为了在经典安全性的可用级别上处理加密数据，例如128位，加密过程引入了明显的数据尺寸扩展 - 密文比本机数据类型的本机汇总大得多。在本文中，我们介绍了Memfhe，它是最新的ring-GSW（Gentry，sahai和Waters）的端子和服务器的第一个加速器。 PIM通过大量加密数据来减轻数据运动问题，同时提供原位执行和FHE多项式操作所需的广泛的并行性。尽管客户端PIM可以同派加密和解密数据，但Server-Pim可以在不解密的情况下处理同型加密数据。 MEMFHE的服务器PIM被管道封装，旨在提供灵活的引导程序，允许根据应用程序要求进行两种加密技术和各种安全级别的安全级别。我们评估MEMFHE的各种安全级别，并将其与基于RING-GSW的FHE的最新CPU实现进行了比较。对于算术操作，MEMFHE的速度比CPU（GPU）快20kX（265倍），并且在使用FHE实施神经网络时，2007倍的吞吐量平均高于2007倍的吞吐量。
The increasing amount of data and the growing complexity of problems has resulted in an ever-growing reliance on cloud computing. However, many applications, most notably in healthcare, finance or defense, demand security and privacy which today's solutions cannot fully address. Fully homomorphic encryption (FHE) elevates the bar of today's solutions by adding confidentiality of data during processing. It allows computation on fully encrypted data without the need for decryption, thus fully preserving privacy. To enable processing encrypted data at usable levels of classic security, e.g., 128-bit, the encryption procedure introduces noticeable data size expansion - the ciphertext is much bigger than the native aggregate of native data types. In this paper, we present MemFHE which is the first accelerator of both client and server for the latest Ring-GSW (Gentry, Sahai, and Waters) based homomorphic encryption schemes using Processing In Memory (PIM). PIM alleviates the data movement issues with large FHE encrypted data, while providing in-situ execution and extensive parallelism needed for FHE's polynomial operations. While the client-PIM can homomorphically encrypt and decrypt data, the server-PIM can process homomorphically encrypted data without decryption. MemFHE's server-PIM is pipelined and is designed to provide flexible bootstrapping, allowing two encryption techniques and various FHE security-levels based on the application requirements. We evaluate MemFHE for various security-levels and compare it with state-of-the-art CPU implementations for Ring-GSW based FHE. MemFHE is up to 20kx (265x) faster than CPU (GPU) for FHE arithmetic operations and provides on average 2007x higher throughput than the state-of-the-art while implementing neural networks with FHE.
