A More Accurate and Robust Binary Ring-LWE Decryption Scheme and Its Hardware Implementation for IoT Devices

Learning with error (LWE) over the ring based on binary distribution (ring-BinLWE) has become a potential Internet-of-Things (IoT) confidentiality solution with its anti-quantum attack properties and uncomplicated calculations. Compared with ring-LWE based on discrete Gaussian distribution, the decr...

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Veröffentlicht in:IEEE transactions on very large scale integration (VLSI) systems 2022-08, Vol.30 (8), p.1007-1019
Hauptverfasser: Xu, Dongdong, Wang, Xiang, Hao, Yuanchao, Zhang, Zhun, Hao, Qiang, Zhou, Zhiyu
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Sprache:eng
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Zusammenfassung:Learning with error (LWE) over the ring based on binary distribution (ring-BinLWE) has become a potential Internet-of-Things (IoT) confidentiality solution with its anti-quantum attack properties and uncomplicated calculations. Compared with ring-LWE based on discrete Gaussian distribution, the decryption scheme of ring-LWE based on binary distribution needs to be re- determined due to the asymmetry of the error distribution. The direct application of the ring-LWE decryption function based on discrete Gaussian distribution can cause serious misjudgment. In this article, we propose a more accurate and robust decryption scheme for ring-BinLWE based on 2's complement ring. Compared with the previous decryption function, the re- derived decryption function significantly improves the decoding rate by 50%. Furthermore, based on the proposed decryption function, high-performance, and lightweight hardware architectures for terminal devices in IoT are, respectively, proposed, which are scalable and can be easily adapted to ring-BinLWE hardware deployment with other parameter sets. When the parameter set is n\,\,= 256, q\,\,= 256, the high-performance implementation consumes 7.6k LUTs, 6.2k FFs, and 2.3k SLICEs on Spartan 6 field-programmable gate array (FPGA) platform. Compared with the previous implementation, our resource overhead increases by only 23% while the decryption accuracy is significantly improved by 50%. The lightweight implementation for parameter set n\,\,= 256, q\,\,= 256 consumes only 230 LUTs, 338 FFs, and 84 SLICEs on the Spartan 6 FPGA platform. Compared with the previous work, the area \times time (AT) is reduced by 47.8%, which is more suitable for deployment on resource-constrained IoT nodes.
ISSN:1063-8210
1557-9999
DOI:10.1109/TVLSI.2022.3174205