Efficiency Analysis of TFHE Fully Homomorphic Encryption Software Library Based on GPU

Yang, Haibin; Yao, Wujun; Li, Wenchao; Wei, Bin

doi:10.1007/978-3-030-15035-8_9

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…Both the libraries focused on optimizing of the boolean gate operations. Recently, Yang et al [49] benchmarked cuFHE and its predecessor TFHE, and analyzed the speedup which we also discuss in our paper (Table I).…”

Section: Related Workmentioning

confidence: 93%

CPU and GPU Accelerated Fully Homomorphic Encryption

Morshed¹,

Aziz²,

Mohammed³

2020

Preprint

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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× for any 32-bit boolean operation and 14.5× for multiplications.

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Section: Related Workmentioning

confidence: 93%

CPU and GPU Accelerated Fully Homomorphic Encryption

Morshed¹,

Aziz²,

Mohammed³

2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Both libraries focused on optimizing the Boolean gate operations. Recently, Yang et al [52] benchmarked cuFHE and its predecessor, TFHE, and analyzed the speedup, which we also discuss in this article (Table 8).…”

Section: Related Work 61 Parallel Framework For Fhementioning

confidence: 99%

Secure Genomic String Search with Parallel Homomorphic Encryption

Aziz,

Tamal,

Mohammed

2024

Information

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Fully homomorphic encryption (FHE) cryptographic systems enable limitless computations over encrypted data, providing solutions to many of today’s data security problems. While effective FHE platforms can address modern data security concerns in unsecure environments, the extended execution time for these platforms hinders their broader application. This project aims to enhance FHE systems through an efficient parallel framework, specifically building upon the existing torus FHE (TFHE) system chillotti2016faster. The TFHE system was chosen for its superior bootstrapping computations and precise results for countless Boolean gate evaluations, such as AND and XOR. Our first approach was to expand upon the gate operations within the current system, shifting towards algebraic circuits, and using graphics processing units (GPUs) to manage cryptographic operations in parallel. Then, we implemented this GPU-parallel FHE framework into a needed genomic data operation, specifically string search. We utilized popular string distance metrics (hamming distance, edit distance, set maximal matches) to ascertain the disparities between multiple genomic sequences in a secure context with all data and operations occurring under encryption. Our experimental data revealed that our GPU implementation vastly outperforms the former method, providing a 20-fold speedup for any 32-bit Boolean operation and a 14.5-fold increase for multiplications.This paper introduces unique enhancements to existing FHE cryptographic systems using GPUs and additional algorithms to quicken fundamental computations. Looking ahead, the presented framework can be further developed to accommodate more complex, real-world applications.

show abstract

CPU and GPU Accelerated Fully Homomorphic Encryption

Morshed

Aziz

Mohammed

2020

2020 IEEE International Symposium on Hardware Oriented Security and Trust (HOST)

View full text Add to dashboard Cite

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) [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× for any 32-bit boolean operation and 14.5× for multiplications.

show abstract

Efficiency Analysis of TFHE Fully Homomorphic Encryption Software Library Based on GPU

Cited by 3 publications

References 7 publications

CPU and GPU Accelerated Fully Homomorphic Encryption

CPU and GPU Accelerated Fully Homomorphic Encryption

Secure Genomic String Search with Parallel Homomorphic Encryption

CPU and GPU Accelerated Fully Homomorphic Encryption

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