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International Journal of Modern Research in Science & Engineering

Post-Quantum Cryptography for Distributed Systems: Challenges, Opportunities, and Implementation Strategies

Chen Ming

Published Jan 1, 2021• 0 citations
Publisher

Abstract

The rapid advancement of quantum computing poses a critical threat to classical cryptographic schemes that secure modern distributed systems. As the deployment of distributed infrastructures—including blockchains, cloud-native applications, and edge networks—continues to grow, there is an urgent need to transition towards quantum-resistant security mechanisms. This paper provides a comprehensive examination of post-quantum cryptographic (PQC) algorithms and their applicability in distributed system architectures. We analyze the impact of quantum threats on key components of distributed systems such as communication protocols, identity management, data integrity, and consensus mechanisms. Furthermore, we evaluate current NIST-standardized PQC algorithms in terms of computational efficiency, scalability, and integration overhead within distributed environments. Challenges in key management, performance trade-offs, and implementation considerations are discussed. Finally, we propose best practices and future directions for secure, quantum-resilient distributed systems.

DOI
10.67228/30716357/ijmrse-2021pii3y2b

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The rapid growth of quantum computing poses a critical threat to classical cryptographic systems. That is due in large part to the fact that they represent efficient solutions for some of the most challenging mathematical problems such as integer factoring or finding discrete logarithms. Schemes currently in wide use including RSA, Diffie–Hellman, and elliptic curve cryptography are particularly susceptible to being attacked by quantum computers, using algorithms like Shor's Algorithm and Grover's Algorithm. The advent of post-quantum cryptography (PQC) provides a promising potential answer to providing longterm security from adversaries using quantum computing capabilities. This paper presents a comprehensive state-of-the-art review of various PQC techniques currently being researched, including lattice-based methods, code-based methods, multivariate methods, hash-based methods, and isogeny-based methods. key challenges that will need to be addressed include computational overhead, large key sizes and implementation complexity. Furthermore the study reviews the current standardization efforts by the National Institute of Standards and Technology related to PQC and discusses some of the very specific practical considerations that need to be considered before widely deploying these techniques. The paper concludes by a complete list of existing research challenges and proposed future work that will assist in the development of efficient and scalable systems that will resist attacks from quantum computers.
As artificial intelligence (AI) systems increasingly underpin critical applications in healthcare, finance, defense, and infrastructure, ensuring their security has become paramount. However, the rapid advancement of quantum computing poses a significant threat to classical cryptographic schemes that currently safeguard these systems. Quantum algorithms such as Shor’s and Grover’s threaten to undermine the confidentiality, integrity, and authentication of data and models that AI systems depend on. In this context, Post-Quantum Cryptography (PQC) has emerged as a crucial area of research aimed at developing cryptographic algorithms that are secure against both classical and quantum adversaries. This paper investigates the intersection of quantum computing threats and AI system vulnerabilities, highlighting how current security mechanisms may fail in a post-quantum era. We explore various categories of PQC—including lattice-based, hash-based, and multivariate polynomial cryptography—and evaluate their applicability to securing AI systems, particularly in scenarios such as model distribution, federated learning, data encryption, and communication in AI-enabled edge and IoT environments. Through analysis of emerging use cases and proposed architectures, the paper outlines how PQC can be integrated into the AI lifecycle to mitigate quantum-era threats. It also identifies key challenges such as computational overhead, integration complexity, and standardization gaps. The study concludes by outlining future research opportunities, including the development of efficient PQC protocols tailored to AI workloads, the creation of quantum-resilient AI pipelines, and the importance of interdisciplinary collaboration to secure AI’s future.
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