论文标题
CPU和GPU加速了完全同态加密
CPU and GPU Accelerated Fully Homomorphic Encryption
论文作者
论文摘要
完全同态加密（FHE）是保护隐私保护的最有前途的技术之一，因为它允许通过加密数据进行任意数量的函数计算。但是，这些系统的计算成本限制了其广泛的应用程序。在本文中，我们的目标是通过设计有效的并行框架来提高方案的性能。特别是，我们选择圆环完全同态加密（TFHE），因为它为无限数量的布尔门（例如，XOR）评估提供了确切的结果。我们首先将门操作扩展到代数电路，例如添加，乘法及其向量和矩阵等效物。其次，我们考虑多核CPU来提高栅极和算术操作的效率。最后，我们将TFHE移植到图形处理单元（GPU）和设备的新型优化，用于使用多种核心的布尔和算术电路。我们还通过实验分析了不同数字表示（16至32位）的CPU和GPU平行框架。我们的GPU实施优于现有技术，对于任何32位布尔操作，它可以达到20倍的速度和14.5倍的乘法。
Fully Homomorphic Encryption (FHE) is one of the most promising technologies for privacy protection as it allows an arbitrary number of function computations over encrypted data. However, the computational cost of these FHE systems limits their widespread applications. In this paper, our objective is to improve the performance of FHE schemes by designing efficient parallel frameworks. In particular, we choose Torus Fully Homomorphic Encryption (TFHE) as it offers exact results for an infinite number of boolean gate (e.g., AND, XOR) evaluations. We first extend the gate operations to algebraic circuits such as addition, multiplication, and their vector and matrix equivalents. Secondly, we consider the multi-core CPUs to improve the efficiency of both the gate and the arithmetic operations. Finally, we port the TFHE to the Graphics Processing Units (GPU) and device novel optimizations for boolean and arithmetic circuits employing the multitude of cores. We also experimentally analyze both the CPU and GPU parallel frameworks for different numeric representations (16 to 32-bit). Our GPU implementation outperforms the existing technique, and it achieves a speedup of 20x for any 32-bit boolean operation and 14.5x for multiplications.
