The rise in technical features and capabilities of consumer electronics (CSE) devices ranging from smartphones, high fidelity (Hifi) radio systems, MP radio sets and IoT-enabled smart devices, have created new possibilities about their use and applications. You tube files can now be downloaded on smartphones and played directly on high fidelity audio radio sets through Bluetooth. There have also been increase in the use of smart phones in diverse areas of life such as in academics, industries etc., where the use of online lectures and video conference meetings have increased in recent times. It is evident that these technologies offer convenience and smart connectivity, on the downside however is the security concern resulting from these communications. Classical cryptography algorithms built into these consumer electronics are prone to quantum computing attacks, and with the steady growth of quantum computing and algorithms, it is only a matter of time before the current classical cryptography algorithms become obsolete. This aim of this paper is to design a light weight Ring Learning With Layered Errors Key Encapsulation Mechanism (RLWLEKEM), a CCA secure lattice based post quantum cryptography algorithm for consumer electronics (CSE) platform. Experiments were conducted on Raspberry Pi 5, which served as a representative testbed for resource constrained CSE devices. Results were gathered using the experimental test bed to compare the performance of RLWLEKEM with some selected cryptography protocols such as code based cryptography (CBC), and classical cryptography algorithms. Performance metrics used for comparison include encryption time, execution time, communication overhead and energy consumption. Experimental results show that RLWLEKEM outperforms the other compared cryptography protocols in terms of time to complete cryptography process, storage/communication overhead and energy consumption and thus was most suitable for deployment as post quantum cryptography key encapsulation mechanism on energy constrained CSE devices.
The rise in technical features and capabilities of consumer electronics (CSE) devices ranging from smartphones, high fidelity (Hifi) radio systems, MP radio sets and IoT-enabled smart devices, have created new possibilities about their use and applications. You tube files can now be downloaded on smartphones and played directly on high fidelity audio radio sets through Bluetooth. There have also been increase in the use of smart phones in diverse areas of life such as in academics, industries etc., where the use of online lectures and video conference meetings have increased in recent times. It is evident that these technologies offer convenience and smart connectivity, on the downside however is the security concern resulting from these communications. Classical cryptography algorithms built into these consumer electronics are prone to quantum computing attacks, and with the steady growth of quantum computing and algorithms, it is only a matter of time before the current classical cryptography algorithms become obsolete. This aim of this paper is to design a light weight Ring Learning With Layered Errors Key Encapsulation Mechanism (RLWLEKEM), a CCA secure lattice based post quantum cryptography algorithm for consumer electronics (CSE) platform. Experiments were conducted on Raspberry Pi 5, which served as a representative testbed for resource constrained CSE devices. Results were gathered using the experimental test bed to compare the performance of RLWLEKEM with some selected cryptography protocols such as code based cryptography (CBC), and classical cryptography algorithms. Performance metrics used for comparison include encryption time, execution time, communication overhead and energy consumption. Experimental results show that RLWLEKEM outperforms the other compared cryptography protocols in terms of time to complete cryptography process, storage/communication overhead and energy consumption and thus was most suitable for deployment as post quantum cryptography key encapsulation mechanism on energy constrained CSE devices.
The proliferation of consumer electronics (CE), from smartphones to IoT-enabled smart devices, has significantly transformed our everyday lives, enhancing convenience and connectivity. However, these transformations to modern life have introduced security vulnerabilities, particularly as traditional cryptographic algorithms face mounting challenges from quantum computing technology. Post-quantum cryptography (PQC) has emerged as a promising solution for providing future-proof security for next-generation CE against the quantum computing threat. PQC algorithms are developed to resist cyberattacks from both classical and quantum computers, ensuring long-term security solutions. This article explores the integration of PQC into next-generation CE devices to ensure confidentiality, integrity, and user privacy. It highlights the vulnerabilities of classical cryptographic methods and underscores the need for transitioning to quantum-resistant solutions. Moreover, the practical implementation of PQC in resource-constrained CE devices is analyzed. Our evaluation shows that optimized PQC incurs negligible overhead on servers (5%), can be up to 12× slower on CE devices, and reduces communication overhead below conventional ECC (1 KB vs. 2.1 KB), while achieving hardware throughput thousands of times higher than RSA. By proactively exploring the opportunities and challenges associated with PQC, this research outlines future directions for implementing robust, scalable, and secure systems, thereby ensuring consumer trust in the evolving technological landscape.
Following the completion of the NIST post-quantum cryptography standardization, Kyber has been adopted as a key encapsulation mechanism (KEM) for quantum-resistant communication. Although lattice-based KEMs provide strong security and efficiency, most existing designs restrict the cyclotomic ring dimension to powers of two, which limits parameter flexibility for heterogeneous and resource-constrained Internet of Things (IoT) devices. In this paper, we propose Fyber, a post-quantum KEM based on the Module Learning With Errors (M-LWE) problem over a module ring defined by the cyclotomic polynomial f(x)=xn−xn/2+1, where n is a product of powers of 2 and 3. This construction enables mixed-radix parameter selection and allows finer-grained trade-offs between security and efficiency. To further improve performance on constrained platforms, we introduce an efficient non-Gaussian sampling method. The proposed KEM supports flexible security-level stratification for IoT applications, achieving reduced public key and ciphertext sizes for selected parameter sets at the cost of moderately increased computational overhead compared to Kyber, and fills intermediate security gaps between existing standardized parameter sets.
Abstract Quantum computing threatens the security foundations of consumer electronics (CE). Preparing CE systems for the post-quantum transition requires quantitative evidence on the performance of standardized and selected post-quantum cryptography (PQC). This paper presents a cross-platform performance analysis of PQC key encapsulation mechanisms (KEMs) and digital signature schemes against classical RSA/ECC baselines. We evaluate execution time and communication and storage overhead on two desktop-class reference platforms (macOS/M4 and Ubuntu/x86-64) and on a Raspberry Pi 4 platform used as a proxy for Linux-based gateway-class CE hubs and gateways. To establish a portable software baseline, the benchmark excludes vendor-specific hardware acceleration such as NEON and AVX. The results show that lattice-based schemes, especially ML-KEM and ML-DSA, provide a favorable measured combination of execution time and object size for gateway-class deployment. By contrast, Classic McEliece imposes large public-key sizes, while SPHINCS+ incurs large signatures despite its conservative hash-based design. Based on these measurements, we provide recommendations for gateway-class CE devices and identify open issues for deeper embedded platforms.
The emergence of quantum computing seriously threatens the security of legacy cryptographic protocols in consumer electronics devices based on the Internet of Things (IoT). These are essential components of mission-critical infrastructures, such as industrial control systems, smart grids, and healthcare. In particular, this work examines how to strengthen the resistance of the IoT to classical and quantum-based cyberattacks by combining deep learning methods with post-quantum cryptography (PQC). To protect key exchange, encryption, and authentication processes in IoT-consumer devices, we will continue to use quantum-resistant cryptographic algorithms, including lattice-based, hash-based, and code-based cryptography. To provide real-time anomaly detection and response capabilities, we suggest a hybrid security framework combining PQC with deep learning-based intrusion detection systems. Our methodology ensures secure communications and data exchanges linked to quantum attacks in the future within IoT systems while leveraging continuous learning over time to allow Intrusion Detection System (IDS) models to adapt to new attack vectors. We suggest optimized algorithms for PQC solutions that are lightweight and suitable for deployment in edge computing nodes and Internet of Things devices, considering the resource-constrained IoT situations where PQC implementation would be difficult. Through comparative studies and simulations, we demonstrate how our integrated PQC-deep learning framework offers an improved IoT security solution that could safeguard vital systems in a world powered by quantum technology. The results shows the key generation time which is 12.5ms, encryption/decryption time 25.3ms, latency 18.7ms, throughput 5000 operations/sec and energy consumption 2.4mJ.
Key exchange mechanisms are foundational to secure communication, yet traditional methods face challenges from quantum computing. The Module-Lattice-Based Key-Encapsulation Mechanism (ML-KEM) is a post-quantum cryptographic key exchange protocol with unknown successful quantum vulnerabilities. This study evaluates the ML-KEM using experimental benchmarks. We implement the ML-KEM in Python for clarity and in C++ for performance, demonstrating the latter’s substantial performance improvements. The C++ implementation achieves microsecond-level execution times for key generation, encapsulation, and decapsulation. Python, while slower, provides a user-friendly introduction to the ML-KEM’s operation. Moreover, our Python benchmark confirmed that the ML-KEM consistently outperformed RSA in execution speed across all tested parameters. Beyond benchmarking, the ML-KEM is shown to handle the computational hardness of the Module Learning With Errors (MLWE) problem, ensuring resilience against quantum attacks, classical attacks, and Artificial Intelligence (AI)-based attacks, since the ML-KEM has no pattern that could be detected. To evaluate its practical feasibility on constrained devices, we also tested the C++ implementation on a Raspberry Pi 4B, representing IoT use cases. Additionally, we attempted to run integration and benchmark tests for the ML-KEM on microcontrollers such as the ESP32 DevKit, ESP32 Super Mini, ESP8266, and Raspberry Pi Pico, but these trials were unsuccessful due to memory constraints. The results showed that while the ML-KEM can operate effectively in such environments, only devices with sufficient resources and runtimes can support its computational demands. While resource-intensive, the ML-KEM offers scalable security across diverse domains such as IoT, cloud environments, and financial systems, making it a key solution for future cryptographic standards.
The rapid advancement of quantum computing poses a critical threat to classical cryptographic algorithms such as RSA and ECC, particularly in Internet of Things (IoT) devices, where secure communication is essential but often constrained by limited computational resources. This paper investigates the feasibility of deploying post-quantum cryptography (PQC) algorithms on resource-constrained devices. In particular, we implement three PQC algorithms -- BIKE, CRYSTALS-Kyber, and HQC -- on a lightweight IoT platform built with Raspberry Pi devices. Leveraging the Open Quantum Safe (\texttt{liboqs}) library in conjunction with \texttt{mbedTLS}, we develop quantum-secure key exchange protocols, and evaluate their performance in terms of computational overhead, memory usage, and energy consumption for quantum secure communication. Experimental results demonstrate that the integration of PQC algorithms on constrained hardware is practical, reinforcing the urgent need for quantum-resilient cryptographic frameworks in next-generation IoT devices. The implementation of this paper is available at https://iqsec-lab.github.io/PQC-IoT/.