摘要
数字签名在信息安全中扮演着至关重要的角色,但传统的数字签名算法在后量子时代面临失效的风险。SPHINCS^(+)作为一种能够抵抗量子计算攻击的数字签名框架,将在后量子时代发挥越来越重要的作用。然而,SPHINCS^(+)的计算速度较慢,难以满足现代密码算法对于高吞吐量和低延时的需求,极大地限制了其实用性。提出了一种基于国产深度计算单元(deep computing unit,DCU)的高效优化方案,以加速由国产哈希算法SM3实例化的SPHINCS^(+)算法。通过提高内存拷贝效率、优化SM3、改进SPHINCS^(+)的计算流程以及采用最佳计算并行度,在DCU上实现了SPHINCS^(+)-SM3的128-f模式。实验结果表明,与传统CPU实现相比,DCU上的实现显著提高了签名生成和验证的吞吐量,分别达到了2603.87倍和1281.98倍的提升,极大地增强了SPHINCS^(+)的计算效率和实用性,并推进了后量子密码算法的国产化进程。在数据流量和大量签名请求的场景下,DCU实现展现出显著优于CPU实现的性能优势。
Digital signatures play a critical role in information security;however,traditional digital signature algorithms are at risk of becoming obsolete in the post-quantum era.SPHINCS^(+),as a digital signature framework resistant to quantum computing attacks,is expected to become increasingly important in this new era.Nevertheless,the relatively slow computational speed of SPHINCS^(+)poses challenges in meeting the high throughput and low latency demands of modern cryptographic applications,significantly limiting its practicality.An efficient optimization strategy based on a domestic DCU(Deep Computing Unit)is presented to accelerate the SPHINCS^(+)algorithm instantiated with the domestic SM3 Hash function.By enhancing memory copy efficiency,optimizing the computational processes of SM3 and SPHINCS^(+),and employing optimal computational parallelism,we implement the 128-f mode of SPHINCS^(+)-SM3 on DCU.Experimental results demonstrate that,compared with traditional CPU implementations,our DCU-based implementation achieves a significant increase in throughput,improving signature generation and verification by 2603.87 times and 1281.98 times,respectively.This substantial improvement in computational efficiency and practicality enhances the feasibility of SPHINCS^(+)and advances the domestic adoption of post-quantum cryptographic algorithms.In scenarios involving high data traffic and large volumes of signature requests,the DCU implementation exhibits significant performance advantages over CPU implementations.
作者
宁祎静
董建阔
周思源
林璟锵
孙思维
郑昉昱
葛春鹏
Ning Yijing;Dong Jiankuo;Zhou Siyuan;Lin Jingqiang;Sun Siwei;Zheng Fangyu;Ge Chunpeng(School of Cyber Science and Technology,University of Science and Technology of China,Hefei 230026;School of Computer Science,Nanjing University of Posts and Telecommunications,Nanjing 210023;School of Cryptology,University of Chinese Academy of Sciences,Beijing 100049;School of Software,Shandong University,Jinan 250100)
出处
《计算机研究与发展》
北大核心
2026年第2期405-418,共14页
Journal of Computer Research and Development
基金
国家自然科学基金项目(62572255,62302238,62372245,62172258)
江苏省自然科学基金项目(BK20220388)
江苏省科技厅重点研发计划产业前瞻与关键核心技术项目(BE2022081)
中国博士后科学基金项目(2022M711689)
腾讯犀牛鸟基金项目(RAGR20240129)。
