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.