| Summary: | 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.
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