The rapid progress of quantum computing poses substantial risks to classical cryptographic mechanisms widely deployed in Big Data processing systems, whose distributed architectures, high computational intensity, and exposure to diverse attack vectors impose stringent requirements on long-term security and reliability. Accordingly, this study aims to develop a risk-oriented framework for assessing post-quantum cryptographic (PQC) algorithms in large-scale data environments. The proposed methodology integrates cryptographic reliability and computational efficiency within a unified multi-criteria decision-making model, incorporating formalised threat modelling, probabilistic analysis of resistance to classical and quantum attacks, robustness evaluation under high computational loads, and quantitative performance metrics, including latency, throughput, resource consumption, and scalability in distributed infrastructures. Based on this framework, a comparative analysis of representative classes of PQC algorithms is conducted in typical Big Data processing scenarios. The results indicate that different PQC families exhibit distinct security–performance trade-offs and that no single algorithm is universally optimal across all operational conditions. The study concludes that a risk-oriented, multi-criteria selection of post-quantum cryptographic mechanisms enhances the overall resilience and operational efficiency of secure Big Data systems, providing a practical foundation for their design, modernisation, and cybersecurity governance in the post-quantum era.
Aktayewa (Wed,) studied this question.