Reviews: SHE: A Fast and Accurate Deep Neural Network for Encrypted Data

Main contribution: The paper shows how to implement an accurate homomorphic ReLU and homomorphic max-pooling operation. This is achieved by combining the idea of logarithmic quantization followed by shifting and adding operations with the basic approach of TFHE (Fast Fully Momorphic Encryption over the Torus). Further they also note that 5 bit representations are sufficient for weights, but the intermediate results of accumulation need 16 bit representation to avoid degrading accuracy. Thus they propose mixed bitwidth accumulators to avoid unnecessary computational costs. By using these few key ideas the authors show how TFHE can now support fast matrix multiplications and convolutions which previously were extremely slow.