DTDS: Dilithium dataset for power analysis

Solemnly Declare: when using this data set to publish papers, books and other works, you must formally quote the papers to which this data set belongs:Citation: YUAN Qingjun, ZHANG Haojin, FAN Haopeng, GAO Yang, WANG Yongjuan. DTDS: Dilithium Dataset for Power Analysis[J]. Journal of Electronics & Information Technology, 2025, 47(8): 2499-2508. doi: 10.11999/JEIT250048Authors: YUAN Qingjun, ZHANG Haojin, FAN Haopeng, GAO Yang, WANG YongjuanAuthor unit:Key Laboratory of Network Cryptography, Henan Province, Information Engineering UniversityKey Laboratory for Intelligent Network and Network Security, Xi’an Jiaotong UniversityCorrespondent: WANG Yongjuan，[e-mail] link：DTDS：用于侧信道能量分析的Dilithium数据集Abstract:   Objective  The development of quantum computing threatens the security of traditional cryptosystems and advances the research and standardisation of post-quantum cryptographic algorithms. The Dilithium digital signature algorithm is designed based on the lattice theory and was selected by USA National Institute of Standards and Technology (NIST) as the standard for post-quantum cryptographic algorithms in 2024. Meanwhile, the side channel analysis of Dilithium, especially the power analysis, has become a current research hotspot. However, the existing power analysis datasets are mainly for classical packet cryptography algorithms, such as AES, etc., and the lack of datasets for novel algorithms, such as Dilithium, restricts the research of side-channel security analysis methods.  Results and Discussions  For this reason, this paper collects and discloses the first power analysis dataset for the Dilithium algorithm, aiming to facilitate the research on power analysis of post-quantum cryptographic algorithms. The dataset is based on the open-source reference implementation of Dilithium, running on a Cortex M4 processor and captured by a dedicated device, and contains 60,000 traces captured during the Dilithium signature process, as well as the signature source data and sensitive intermediate values corresponding to each trace.  Conclusions  The constructed DTDS dataset is further visualised and analysed, and the execution process of the random polynomial generation function polyz_unpack and its effect on the traces are investigated in detail. Finally, the dataset is modelled and tested using template analysis and deep learning analytics to verify the validity and usefulness of the dataset.

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引用格式：袁庆军, 张浩锦, 范浩鹏, 高扬, 王永娟. DTDS：面向能量分析的Dilithium数据集[J]. 电子与信息学报, 2025, 47(8): 2499-2508. DOI: 10.11999/JEIT250048
作者：袁庆军, 张浩锦, 范浩鹏, 高扬, 王永娟
作者单位：河南省网络密码重点实验室（信息工程大学）；西安交通大学智能网络与网络安全重点实验室
通讯作者：王永娟，邮箱：[e-mail]
原始链接：DTDS：用于侧信道能量分析的Dilithium数据集
摘要：
研究背景与目标：量子计算的发展对传统密码体制的安全性构成威胁，推动了后量子密码算法的研究与标准化进程。Dilithium数字签名算法基于格理论设计，并于2024年被美国国家标准与技术研究院（National Institute of Standards and Technology, NIST）选为后量子密码算法标准。与此同时，针对Dilithium的侧信道分析，尤其是能量分析，已成为当前的研究热点。然而，现有能量分析数据集主要面向AES等经典分组密码算法，针对新型密码算法（如Dilithium）的数据集匮乏，制约了侧信道安全分析方法的研究。
结果与讨论：为此，本文采集并公开了首个面向Dilithium算法的能量分析数据集，旨在推动后量子密码算法能量分析方向的研究。本数据集基于Dilithium的开源参考实现，运行于Cortex M4处理器上，由专用设备采集得到，包含60000条Dilithium签名过程中的能量轨迹，以及每条轨迹对应的签名源数据与敏感中间值。
结论：对构建的DTDS数据集进行了可视化与分析，详细研究了随机多项式生成函数polyz_unpack的执行过程及其对能量轨迹的影响。最终，通过模板分析与深度学习分析对数据集进行建模与测试，验证了本数据集的有效性与实用性。

搜集汇总

背景与挑战

背景概述

DTDS数据集是首个针对后量子密码算法Dilithium的侧信道能量分析数据集，包含6万条签名过程中的捕获轨迹及相关数据，旨在促进后量子密码算法的能量分析研究。数据集基于开源实现并在Cortex M4处理器上运行，通过专用设备捕获，为侧信道安全分析方法研究提供了重要资源。