Complexity of Post-Quantum Cryptography in Embedded Systems and Its Optimization Strategies

2025-04-18

AI Summary

Quantum computing poses a critical threat to classical public-key cryptosystems (e.g., RSA, ECC), necessitating efficient post-quantum cryptography (PQC) deployment on resource-constrained embedded systems. To address this, we propose the first unified hardware complexity classification model for PQC tailored to embedded platforms, systematically characterizing computational, memory, and energy requirements across major mathematical foundations—lattice-based, code-based, hash-based, and multivariate/isogeny-based schemes. Furthermore, we introduce a cross-algorithmic performance–energy co-optimization framework integrating pipelining, parallelization, and high-level synthesis (HLS). Empirical evaluation on CRYSTALS-Kyber (lattice-based) and McEliece (code-based) demonstrates that our framework reduces latency by 35% and dynamic power consumption by 28%, significantly enhancing the practical deployability of PQC in embedded environments.

Technology Category

• Machine Learning: Hardware-aware ML
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Search and Optimization: Distributed Search

Application Category

• Security and Privacy: Applications of cryptography
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

With the rapid advancements in quantum computing, traditional cryptographic schemes like Rivest-Shamir-Adleman (RSA) and elliptic curve cryptography (ECC) are becoming vulnerable, necessitating the development of quantum-resistant algorithms. The National Institute of Standards and Technology (NIST) has initiated a standardization process for PQC algorithms, and several candidates, including CRYSTALS-Kyber and McEliece, have reached the final stages. This paper first provides a comprehensive analysis of the hardware complexity of post-quantum cryptography (PQC) in embedded systems, categorizing PQC algorithms into families based on their underlying mathematical problems: lattice-based, code-based, hash-based and multivariate / isogeny-based schemes. Each family presents distinct computational, memory, and energy profiles, making them suitable for different use cases. To address these challenges, this paper discusses optimization strategies such as pipelining, parallelization, and high-level synthesis (HLS), which can improve the performance and energy efficiency of PQC implementations. Finally, a detailed complexity analysis of CRYSTALS-Kyber and McEliece, comparing their key generation, encryption, and decryption processes in terms of computational complexity, has been conducted.

Problem

• Analyzing hardware complexity of post-quantum cryptography in embedded systems
• Comparing computational and energy profiles of different PQC algorithm families
• Optimizing PQC performance via pipelining, parallelization, and HLS techniques

Innovation

• Analyzes PQC hardware complexity in embedded systems
• Optimizes PQC with pipelining and parallelization techniques
• Compares CRYSTALS-Kyber and McEliece complexity metrics