CPU and GPU Accelerated Fully Homomorphic Encryption

• Published in: 2020 IEEE International Symposium on Hardware Oriented Security and Trust (HOST) pp. 142 - 153
• Main Authors: Morshed, Toufique, Aziz, Md Momin Al, Mohammed, Noman
• Format: Conference Proceeding
• Language: English
• Published: IEEE 07-12-2020
• Subjects: Central Processing Unit; Encryption; Fully Homomorphic Encryption; GPU parallelism; Graphics processing units; Logic gates; Optimization; Parallel FHE Framework; Parallel processing; Secure computation on GPU; Servers
• DOI: 10.1109/HOST45689.2020.9300288

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) [1] 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 [1], and it achieves a speedup of 20\times for any 32-bit boolean operation and 14.5\times for multiplications.