Implementation of a Data-Parallel Approach on a Lightweight Hash Function for IoT Devices

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Publicado en:Mathematics vol. 13, no. 5 (2025), p. 734
Autor principal: Sevin, Abdullah
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MDPI AG
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100 1 |a Sevin, Abdullah 
245 1 |a Implementation of a Data-Parallel Approach on a Lightweight Hash Function for IoT Devices 
260 |b MDPI AG  |c 2025 
513 |a Journal Article 
520 3 |a The Internet of Things is used in many application areas in our daily lives. Ensuring the security of valuable data transmitted over the Internet is a crucial challenge. Hash functions are used in cryptographic applications such as integrity, authentication and digital signatures. Existing lightweight hash functions leverage task parallelism but provide limited scalability. There is a need for lightweight algorithms that can efficiently utilize multi-core platforms or distributed computing environments with high degrees of parallelization. For this purpose, a data-parallel approach is applied to a lightweight hash function to achieve massively parallel software. A novel structure suitable for data-parallel architectures, inspired by basic tree construction, is designed. Furthermore, the proposed hash function is based on a lightweight block cipher and seamlessly integrated into the designed framework. The proposed hash function satisfies security requirements, exhibits high efficiency and achieves significant parallelism. Experimental results indicate that the proposed hash function performs comparably to the BLAKE implementation, with slightly slower execution for large message sizes but marginally better performance for smaller ones. Notably, it surpasses all other evaluated algorithms by at least 20%, maintaining a consistent 20% advantage over Grostl across all data sizes. Regarding parallelism, the proposed PLWHF achieves a speedup of approximately 40% when scaling from one to two threads and 55% when increasing to three threads. Raspberry Pi 4-based tests for IoT applications have also been conducted, demonstrating the hash function’s effectiveness in memory-constrained IoT environments. Statistical tests demonstrate a precision of ±0.004, validate the hypothesis in distribution tests and indicate a deviation of ±0.05 in collision tests, confirming the robustness of the proposed design. 
653 |a Cryptography 
653 |a Parallel processing 
653 |a Software 
653 |a Embedded systems 
653 |a Internet of Things 
653 |a Digital signatures 
653 |a Number systems 
653 |a Communication 
653 |a Hash based algorithms 
653 |a Statistical tests 
653 |a Confidentiality 
653 |a Design 
653 |a Blockchain 
653 |a Algorithms 
653 |a Energy consumption 
653 |a Access control 
653 |a Cybersecurity 
653 |a Distributed processing 
653 |a Multimedia communications 
773 0 |t Mathematics  |g vol. 13, no. 5 (2025), p. 734 
786 0 |d ProQuest  |t Engineering Database 
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