The advent of quantum computing threatens the security of traditional encryption algorithms, motivating the development of post-quantum cryptography (PQC). In 2024, the National Institute of Standards and Technology (NIST) standardized several PQC algorithms, marking an important milestone in the transition toward quantum-resistant security. Blockchain systems fundamentally rely on cryptographic primitives to guarantee data integrity and transaction authenticity. However, widely used algorithms such as ECDSA, employed in Bitcoin, Ethereum, and other networks, are vulnerable to quantum attacks. Although adopting PQC is essential for long-term security, its computational overhead in blockchain environments remains largely unexplored. In this work, we propose a methodology for benchmarking both PQC and traditional cryptographic algorithms in blockchain contexts. We measure signature generation and verification times across diverse computational environments and simulate their impact at scale. Our evaluation focuses on PQC digital signature schemes (ML-DSA, Dilithium, Falcon, Mayo, SLH-DSA, SPHINCS+, and Cross) across security levels 1 to 5, comparing them to ECDSA, the current standard in Bitcoin and Ethereum. Our results indicate that PQC algorithms introduce only minor performance overhead at security level 1, while in some scenarios they significantly outperform ECDSA at higher security levels. For instance, ML-DSA achieves a verification time of 0.14 ms on an ARM-based laptop at level 5, compared to 0.88 ms for ECDSA. We also provide an open-source implementation to ensure reproducibility and encourage further research.
翻译:量子计算的出现威胁着传统加密算法的安全性,这推动了后量子密码学的发展。2024年,美国国家标准与技术研究院标准化了多项后量子密码算法,标志着向抗量子安全过渡的重要里程碑。区块链系统从根本上依赖密码学原语来保证数据完整性和交易真实性。然而,比特币、以太坊等网络中广泛使用的算法(如ECDSA)易受量子攻击。尽管采用后量子密码学对于长期安全至关重要,但其在区块链环境中的计算开销在很大程度上仍未得到充分探索。在本研究中,我们提出了一种在区块链场景中对后量子密码学和传统密码算法进行基准测试的方法。我们测量了不同计算环境下的签名生成和验证时间,并模拟了它们在大规模场景下的影响。我们的评估聚焦于安全等级1至5的后量子数字签名方案,并将其与当前比特币和以太坊的标准ECDSA进行比较。结果表明,在安全等级1下,后量子密码算法仅引入轻微的性能开销,而在某些场景中,它们在更高安全等级下显著优于ECDSA。例如,在基于ARM的笔记本电脑上,ML-DSA在等级5下的验证时间为0.14毫秒,而ECDSA为0.88毫秒。我们还提供了开源实现以确保可复现性并促进进一步研究。