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Secure and Robust Internet of Things with High-Speed Implementation of PRESENT and GIFT Block Ciphers on GPU.

Authors :
Kim, Hyunjun
Eum, Siwoo
Lee, Wai-Kong
Lee, Sokjoon
Seo, Hwajeong
Source :
Applied Sciences (2076-3417); Oct2022, Vol. 12 Issue 20, pN.PAG-N.PAG, 15p
Publication Year :
2022

Abstract

With the advent of the Internet of Things (IoT) and cloud computing technologies, vast amounts of data are being created and communicated in IoT networks. Block ciphers are being used to protect these data from malicious attacks. Massive computation overheads introduced by bulk encryption using block ciphers can become a performance bottleneck of the server, requiring high throughput. As the need for high-speed encryption required for such communications has emerged, research is underway to utilize a graphics processor for encryption processing based on the high processing power of the GPU. Applying bit-slicing of lightweight ciphers was not covered in the previous implementation of lightweight ciphers on GPU architecture. In this paper, we implemented PRESENT and GIFT lightweight block ciphers GPU architectures. It minimizes the computation overhead caused by optimizing the algorithm by applying the bit-slicing technique. We performed practical analysis by testing practical use cases. We tested PRESENT-80, PRESENT-128, GIFT-64, and GIFT-128 block ciphers in RTX3060 platforms. The throughput of the exhaustive search are 553.932 Gbps, 529.952 Gbps, 583.859 Gbps, and 214.284 Gbps for PRESENT-80, PRESENT-128, GIFT-64, and GIFT-128, respectively. For the case of data encryption, it achieved 24.264 Gbps, 24.522 Gbps, 85.283 Gbps, and 10.723 Gbps for PRESENT-80, PRESENT-128, GIFT-64, and GIFT-128, respectively. Specifically, the proposed implementation of a PRESENT block cipher is approximately 4× higher performance than the latest work that implements PRESENT block cipher. Lastly, the proposed implementation of a GIFT block cipher on GPU is the first implementation for the server environment. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
20763417
Volume :
12
Issue :
20
Database :
Complementary Index
Journal :
Applied Sciences (2076-3417)
Publication Type :
Academic Journal
Accession number :
159868951
Full Text :
https://doi.org/10.3390/app122010192