Accelerating Homomorphic Full Adder Based on FHEW Using Multicore CPU and GPUs

Lei, Xinya; Guo, Ruixin; Zhang, Feng; Wang, Lizhe; Xu, Rui; Qu, Guangzhi

doi:10.1109/hpcc/smartcity/dss.2019.00351

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…Lei et al ported FHEW-V2 [25] to GPU [51] and extended the Boolean implementation to 30-bit addition and 6-bit multiplication with a speed up to ≈2.5. Since TFHE extends FHEW and performs better than its predecessor, we consider TFHE as our baseline framework.…”

Section: Related Work 61 Parallel Framework For Fhementioning

confidence: 99%

Secure Genomic String Search with Parallel Homomorphic Encryption

Aziz,

Tamal,

Mohammed

2024

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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.

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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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“…Lei et al ported FHEW-V2 [23] to GPU [48] and extended the boolean implementation to 30-bit addition and 6-bit multiplication with a speed up ≈ 2.5. Since TFHE extends FHEW and performs better than it predecessor, we consider TFHE as our baseline framework.…”

Section: Related Workmentioning

confidence: 99%

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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Data Security and Privacy Preserving with Augmented Homomorphic Re-Encryption Decryption (AHRED) Algorithm in Big Data Analytics

Shoba

Parameswari

2021

2021 Third International Conference on Inventive Research in Computing Applications (ICIRCA)

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Accelerating Homomorphic Full Adder Based on FHEW Using Multicore CPU and GPUs

Cited by 4 publications

References 13 publications

Secure Genomic String Search with Parallel Homomorphic Encryption

Secure Genomic String Search with Parallel Homomorphic Encryption

CPU and GPU Accelerated Fully Homomorphic Encryption

Data Security and Privacy Preserving with Augmented Homomorphic Re-Encryption Decryption (AHRED) Algorithm in Big Data Analytics

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