“…In addition, Differential Privacy (DP) [60], [7], [53] and Secure Enclave (SE) [45], [51], [10], [75] are also explored to protect data security and privacy in neural networks. In order to deal with different properties of linearity (weighted sum and convolution functions) and nonlinearity (activation and pooling functions) in neural network computations, several efforts have been made to orchestrate multiple cryptographic techniques to achieve better performance [74], [43], [38], [48], [56], [44], [76], [18], [73], [47], [71], [16], [12], [41], [54], [46]. Among them, the schemes with HE-based linear computations and GC-based nonlinear computations (called the HE-GC neural network framework hereafter) demonstrate superior performance [43], [38], [44], [46].…”