Data Science Approaches to Quantum Vulnerability Assessment and Post-Quantum Cryptography Schemes

Abstract

Quantum computing poses a threat to classical cryptographic systems, and proactive measures need to be taken to analyze vulnerabilities and design secure cryptographic protocols. This study provides an end-to-end review of data science methods being utilized for the quantum-vulnerability-assessment coupled with a survey of post-quantum cryptography (PQC) protocols. We begin by characterizing the quantum computing advances that imperil widely used public-key algorithms, highlighting the urgency of determining which digital assets are at risk. We then clarify how data-driven approaches—specifically, statistical analysis, machine learning, and large-scale cryptographic data audits—can identify probable weaknesses and guide risk remediation. These techniques enable the comprehension of the extent of quantum threats through cryptographic usage pattern analysis, and enable assessing algorithmic strength against quantum attack models, and prioritizing systems for near-term cryptographic upgrade. The highlight of the paper is a survey of the most well-known PQC algorithms that are secure against quantum attacks like lattice-based, code-based, multivariate, and hash-based constructions. For every class, we explain the tough issues underlying and the current state of security and performance. We discuss the real roadblocks to PQC deployment, ranging from implementation to performance concerns, and talk about how data analysis is can help inform efforts along the way. The study concludes by noting that protecting digital infrastructure from impending quantum capability requires the coming together of stringent data-driven research and cryptographic innovation. Integrating perspectives from cryptography and data science, this study explores how combining these disciplines can help enhance security in the emerging post-quantum world, while recognizing the associated challenges and complexities.