Biblio

This paper presents a novel approach for a facial security system using elliptic curve cryptography. Face images extracted from input video are encrypted before sending to a remote server. The input face images are completely encrypted by mapping each pixel value of the detected face from the input video frame to a point on an elliptic curve. The original image can be recovered when needed using the elliptic curve cryptography decryption function. Specifically, we modify point multiplication designed for projective coordinates and apply the modified approach in affine coordinates to speed up scalar point multiplication operation. Image encryption and decryption operations are also facilitated using our existing scheme. Simulation results on Visual Studio demonstrate that the proposed systems help accelerate encryption and decryption operations while maintaining information confidentiality.

This paper proposes a configurable architecture of butterfly unit (BU) supporting number theoretic transform (NTT) and inverse NTT (INTT) accelerators in the ring learning with error based homomorphic encryption. The proposed architecture is fully pipelined and carefully optimized the critical path delay. To compare with related works, several BU designs of different bit-size specific primes are synthesized and successfully placed-and-routed on the Xilinx Zynq UltraScale+ ZCU102 FPGA platform. Implementation results show that the proposed BU designs achieve 3× acceleration with more efficient resource utilization compared with previous works. Thus, the proposed BU architecture is worthwhile to develop NTTINTT accelerators in advanced homomorphic encryption systems.