DSCAD: Dilithium Side-Channel Attacks Dataset

Introduction：There is a lack of publicly available and standardized side-channel datasets to support the practical application of deep learning methods. This gap severely limits the development of relevant technologies. To address this issue, we proposes the first side-channel attack-specific dataset for the Dilithium—Dilithium Side-Channel Attacks Dataset (DSCAD), providing an experimental foundation for the application of deep learning in post-quantum cryptography research.Experimental environment：The DSCAD dataset was collected using the ChipWhisperer UFO platform, equipped with an STM32 F405 RGTx microprocessor running at 25 MHz and a 10 MHz passive crystal oscillator. Power consumption data was captured using a Pico 3206D oscilloscope with a 125 MHz sampling rate and an 8-bit mode. A BLP-48+ 50M low-pass filter was used to reduce noise. The Dilithium open-source reference implementation, set to NIST security level 2, was compiled using the gcc-arm cross-compiler with the -O1 optimization to simulate real-world scenarios.Target Operation：The dataset focuses on capturing the power traces during the storage of the sensitive polynomial $\mathbf{u}$ in the Dilithium implementation. Specifically, let \( u_{i,j} \), \( c_j \), and $(\mathbf{s}_1)_{i,j}$ be the coefficients of the polynomials $\mathbf{u}$ , $\mathbf{c}$ , and $(\mathbf{s}_1)$, where \( 1 \leq i \leq 4,1 \leq j \leq 256 \). They satisfy the following Equation (1):$$u_{i,j}=(c_j(\mathbf{s}_1)_{i,j}-c_j(\mathbf{s}_1)_{i,j}q^{-1})\mathrm{mod}^\pm2^{32}\cdot q)>>32(1)$$where \( q = 8380417 \) and $q^{-1}= 58728449$ are precomputed constants. The dataset includes 50,000 power traces, each with 40,000 samples, covering 1024 storage operations of $\mathbf{u}$. Out of these, 40,000 traces are used for signing random plaintexts with a random private key, while the remaining 10,000 traces are used to sign with a fixed private key to attempt key recovery.

引言：当前缺乏可支撑深度学习方法实际应用的公开可用且标准化的侧信道数据集，这一缺口严重制约了相关技术的发展。为解决这一问题，我们构建了首款针对Dilithium的专属侧信道攻击数据集——Dilithium侧信道攻击数据集（Dilithium Side-Channel Attacks Dataset，DSCAD），为深度学习在后量子密码学研究中的应用提供实验基础。
实验环境：本数据集基于ChipWhisperer UFO平台采集，该平台搭载工作主频为25 MHz的STM32 F405 RGTx微处理器与10 MHz无源晶振。功耗数据通过Pico 3206D示波器采集，采样率为125 MHz，采用8位采样模式。实验使用BLP-48+ 50M低通滤波器以降低噪声干扰。本研究采用配置为NIST安全等级2的Dilithium开源参考实现，通过gcc-arm交叉编译器以-O1优化级别进行编译，以模拟真实应用场景。
目标操作：本数据集聚焦采集Dilithium实现中敏感多项式$mathbf{u}$存储阶段的功耗轨迹。具体而言，设$u_{i,j}$、$c_j$与$(mathbf{s}_1)_{i,j}$分别为多项式$mathbf{u}$、$mathbf{c}$及$(mathbf{s}_1)$的系数，其中$1 leq i leq 4$且$1 leq j leq 256$。其满足如下式(1)：
$$u_{i,j}=left(left(c_j(mathbf{s}_1)_{i,j} - c_j(mathbf{s}_1)_{i,j}q^{-1}
ight)mod^pm 2^{32} cdot q
ight) >> 32 ag{1}$$
其中$q=8380417$与$q^{-1}=58728449$为预计算常数。本数据集共包含50000条功耗轨迹，每条轨迹含40000个采样点，覆盖1024次$mathbf{u}$存储操作。其中40000条轨迹用于使用随机私钥对随机明文进行签名，剩余10000条轨迹则采用固定私钥进行签名，以尝试实现密钥恢复。

搜集汇总

背景与挑战

背景概述

DSCAD是一个专门针对Dilithium后量子密码算法的侧信道攻击数据集，旨在解决公开标准化数据集的缺乏问题，为深度学习在侧信道分析中的应用提供实验基础。该数据集包含50,000条功耗轨迹，每条40,000个样本，聚焦于敏感多项式存储操作，使用ChipWhisperer平台采集，数据量达15.11 GB，支持随机和固定私钥签名场景，以促进后量子密码研究。