基于GPU的全同态加密软件库调试与分析

doi:10.3969/j.issn.1671-1122.2019.06.010

信息网络安全 ›› 2019, Vol. 19 ›› Issue (6): 76-83.doi: 10.3969/j.issn.1671-1122.2019.06.010

基于GPU的全同态加密软件库调试与分析

刘文超¹, 潘峰¹(), 杨晓元^1,², 周潭平^1,²

1. 武警工程大学密码工程学院,陕西西安 710086
2. 网络与信息安全武警部队重点实验室,陕西西安 710086

收稿日期:2018-12-05 出版日期:2019-06-10 发布日期:2020-05-11
作者简介:
作者简介：刘文超（1994—）,男,甘肃,硕士研究生,主要研究方向为全同态密码、并行计算;潘峰（1967—）,男,北京,教授,硕士,主要研究方向为对称密码;杨晓元（1959—）,男,陕西,教授,硕士,主要研究方向为对称密码;周潭平（1989—）,男,江西,讲师,博士,主要研究方向为全同态密码。
基金资助:
国家重点研发计划 [2017YFB0802000];国家自然科学基金[U1636114]

Debug and Analysis of Fully Homomorphic Encryption Library Based on GPU

Wenchao LIU¹, Feng PAN¹(), Xiaoyuan YANG^1,², Tanping ZHOU^1,²

1. School of Cryptographic Engineering, Engineering University of the Chinese People’s Armed Police, Xi’an Shaanxi 710086, China
2. Key Laboratory of Network and Information Security of the Chinese People’s Armed Police, Xi’an Shaanxi 710086, China

Received:2018-12-05 Online:2019-06-10 Published:2020-05-11

摘要/Abstract

摘要：

全同态加密能够较好的解决云计算中的隐私保护问题,但目前效率较低仍然是制约全同态加密实用化的瓶颈。基于格的全同态加密方案中存在大量独立矩阵和向量运算,利用GPU适合大量独立数据运算的特点,可以大幅提升全同态加密方案的同态运算效率。文章分析了全同态加密算法的结构并验证了全同态加密软件库的可靠性,对自举过程中噪音的变化和不同参数的正确性进行分析,对全同态加密软件算法库TFHE以及其对应的GPU版本cuFHE,分别进行了调试和分析。实验结果表明,GPU版本的cuFHE运算速度是CPU版本TFHE运算速度的4.5倍,利用GPU可以大幅提升全同态加密方案的运行速度。

关键词: 全同态加密, TFHEW, GPU, 代码库调试

Abstract:

Fully homomorphic encryption can solve the privacy protection problem in cloud computing well, but the low efficiency is still the bottleneck of the practical application of full homomorphic encryption. There are a large number of independent matrix and vector operations in the lattice-based homomorphic encryption scheme and GPU is suitable for processing large number of independent data operations, which can greatly improve the homomorphic operation efficiency of the homomorphic encryption scheme. This paper analyzes the structure of the homomorphic encryption algorithm, verifies the reliability of the homomorphic encryption software library and analysis of noise changes and correctness of different parameters during the bootstrap process. The homomorphic encryption software library TFHE and its corresponding GPU version cuFHE was debugged and analyzed separately. The experimental results show that the GPU version of the TFHE operation speed is 4.5 times that of the CPU version TFHE. The GPU can greatly improve the homomorphic running speed of the homomorphic encryption scheme.

Key words: fully homomorphic encryption, TFHEW, GPU, code library debugging

中图分类号:

TP309

刘文超, 潘峰, 杨晓元, 周潭平. 基于GPU的全同态加密软件库调试与分析[J]. 信息网络安全, 2019, 19(6): 76-83.

Wenchao LIU, Feng PAN, Xiaoyuan YANG, Tanping ZHOU. Debug and Analysis of Fully Homomorphic Encryption Library Based on GPU[J]. Netinfo Security, 2019, 19(6): 76-83.

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参考文献 13

[1]	WANG Yongjian, ZHANG Jian, CHENG Shaoyu, et al.An Improved Design of Homomorphic Encryption for Cloud Computin[J]. Netinfo Security, 2017, 17(3): 21-26.
	王永建,张健,程少豫,等.面向云计算的同态加密改进设计[J].信息网络安全,2017,17(3):21-26.
[2]	HUANG H, GONG T, CHEN P, et al.Secure Two-party Distance Computation Protocol Based on Privacy Homomorphism and Scalar Product in Wireless Sensor Networks[J]. Tsinghua Science and Technology, 2016, 21(4): 385-396.
[3]	LV Haifeng, DING Yong, DAI Yanhong, et al.Survey on LWE-based Fully Homomorphic Encryption Scheme[J]. Netinfo Security, 2015, 15(1): 32-38.
	吕海峰,丁勇,代洪艳,等. LWE上的全同态加密方案研究[J].信息网络安全,2015,15(1):32-38.
[4]	GENTRY C.Fully Homomorphic Encryption Using Ideal Lattices[C]//ACM. The 41st ACM Symposium on Theory of Computing, May 31-June 2 , 2009 Washington, DC, USA. New York: ACM, 2009: 169-178.
[5]	ZHOU Tanping, YANG Xiaoyuan, WEI Zhang, et al. Efficient Fully Homomorphic Encryption with Circularly Secure Key Switching Process[EB/OL]. , 2018-3-1.
[6]	GENTRY C, SAHAI A, WATERS B.Homomorphic Encryption from Learning with Errors: Conceptually-simpler, Asymptotically-faster, Attribute-based, Advances in Cryptology-CRYPTO 2013[M]. Berlin Heidelberg: Springer, 2013.
[7]	CHILLOTTI I, GAMA N, GEORGIEVA M, et al.Faster Fully Homomorphic Encryption: Bootstrapping in Less than 0.1 seconds[C]//Springer. Advances in Cryptology-ASIACRYPT 2016: 22nd International Conference on the Theory and Application of Cryptology and Information Security, December 4-8, 2016, Hanoi, Vietnam. Berlin Heidelberg : Springer, 2016: 3-33.
[8]	ZHOU T P, YANG X Y, LIU L F, et al. Faster Bootstrapping with Multiple Addends[EB/OL]. , 2017-3-15.
[9]	WANG W, HU Y, CHEN L, et al.Accelerating Fully Homomorphic Encryption Using GPU[C]//IEEE. High PERFORMANCE Extreme Computing, September 10-12, 2013, Waltham, USA. New York: IEEE, 2013: 1-5.
[10]	Dai W, Doröz Y, Sunar B.Accelerating NTRU based homomorphic encryption using GPUs[C]//IEEE. IEEE High PERFORMANCE Extreme Computing Conference, September 15-17, 2015, Waltham, MA USA. Waltham, MA USA. New York: IEEE, 2015: 1-6.
[11]	DAI W, SUNAR B. cuHE: A Homomorphic Encryption Accelerator Library[C]//Springer. International Conference on Cryptography and Information Security in the Balkans, September 3-4, 2010, Koper, Slovenia. Berlin Heidelberg : Springer, 2015: 169-186.
[12]	TFHE. Fast Fully Homomorphic Encryption Library over the Torus[EB/OL]. , 2017-5-2.
[13]	DAI W. CUDA-accelerated Fully Homomorphic Encryption Library[EB/OL]. , 2018-3-1.

内存	访问权限	访问范围	生命周期
寄存器内存	R/W	单线程	线程
常量内存	R	所有线程?主机	应用
纹理内存	R	所有线程?主机	应用
共享内存	R/W	块中的所有线程	块
本地内存	R/W	单线程	线程
全局内存	R/W	所有线程?主机	应用

算法	KeyGen	Enc	Dec	Nand
TFHE	2837.00 ms	0.023 ms	1.07 ms	13.00 ms
cuFHE	2758.32 ms	0.034 ms	0.045 ms	2.90 ms

基于GPU的全同态加密软件库调试与分析

Debug and Analysis of Fully Homomorphic Encryption Library Based on GPU

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