CPU and GPU accelerated fully homomorphic encryption

Thumbnail Image
Date
2019-11
Authors
Tamal, Md Toufique Morshed
Journal Title
Journal ISSN
Volume Title
Publisher
Abstract
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 thesis, 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 techniques, and it achieves a speedup of 20x for any 32-bit boolean operation and 14.5x for multiplications.
Description
Keywords
Fully Homomorphic Encryption, GPU parallelism, Secure computation on GPU, Parallel FHE Framework
Citation