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Lattice-based cryptography continues to dominate in the second-round finalists of the National Institute of Standards and Technology post-quantum cryptography standardization process. Computational efficiency is primarily considered to evaluate promising candidates for final round selection. In lattice-based cryptosystems, polynomial multiplication is the most expensive computation and critical to improve the performance. This paper proposes an efficient number theoretic transform (NTT) architecture to accelerate the polynomial multiplication. The proposed design applies mixed-radix multi-path delay feedback architecture and flexibly adopts various polynomial sizes. Configurable NTT design is realized to perform forward and inverse NTT computations on a unified hardware, which is then used to develop an efficient polynomial multiplier. The proposed architectures were successfully accelerated on several Xilinx FPGA platforms to directly compare with state-of-the-art works. The implementation results show that the proposed NTT architectures have comparable area-time product and demonstrate $1.7\sim 17\times $ performance improvement, and the proposed polynomial multipliers achieve higher performance compared with previous works. Experimental results confirmed the proposed design’s applicability for high-speed large-scale data cryptoprocessors.

With explosive era of machine learning development, human data, such as biometric images, videos, and particularly facial information, have become an essential training resource. The popularity of video surveillance systems and growing use of facial images have increased the risk of leaking personal information. On the other hand, traditional cryptography systems are still expensive, time consuming, and low security, leading to be threatened by the foreseeable attacks of quantum computers. This paper proposes a novel approach to fully protect facial images extracted from videos based on a post-quantum cryptosystem named NewHope cryptography. Applying the proposed technique to arrange input data for encryption and decryption processes significantly reduces encryption and decryption times. The proposed facial security system was successfully accelerated using data-parallel computing model on the recently launched Nvidia GTX 2080Ti Graphics Processing Unit (GPU). Average face frame extracted from video ( $190\times 190$ pixel) required only $2.2~ms$ and $2.7~ms$ total encryption and decryption times with security parameters $n=1024$ and $n=2048$ , respectively, which is approximately 9 times faster than previous approaches. Analysis results of security criteria proved that the proposed system offered comparable confidentiality to previous systems.

This paper presents a high efficiency architecture of number theoretic transform (NTT) for ring learning with error (R-LWE) based cryptosystems. A customized multi-path delay feedback (MDF) NTT architecture is proposed with k-parallel pipelined data paths to perform polynomial multiplication effectively. The design was successfully accelerated on Vertex-7 FPGA platform and significantly reduced the cryptosystem's processing time, which achieved approximately 3 times smaller area-time-product (ATP) than previous approaches. The implementation results confirmed the proposed architecture's applicability for high-speed large data security applications.