## 1 Introduction  
MPC was proposed by [PERSON] in 1982 with the millionaire's question [1]. MPC makes sure that participants receive the public function's output in accordance with the protocol but cannot learn any intermediate information in order to reveal the input of other participants or any valuable private information. Data are the foundation for machine learning, and MPC offers a secure computing environment for data using, making it possible to combine the data assets of various parties to train more precise models, which is beneficial for the practical application of machine learning. The focus of MPC research has shifted to support secure computation frameworks for neural networks. By designing cryptographic protocols for linear functions as well as nonlinear activation functions for each layer of neural networks, MPC can protect the data assets of the parties involved in the training process. At present, frameworks have been able to provide secure training and secure inference for deep neural networks (DNN) and convolutional neural networks (CNN), such as security inference based on CryptFlow [2] for detecting lung diseases based on chest X-ray images and using CT images for tumor diagnosis [3]; SecureNN [4] supports security training and secure inference as a 3-party MPC framework with semi-honest; ABY3 [5] supports MPC protocols with the conversion of Arithmetic sharing, Yao's sharing, and Boolean sharing; MinioNN [6], a privacy-preserving inference framework based on Oblivious Transfer (OT); in addition, Crypten [7], a MPC software framework with Pytorch that supports secure inference with GPU; EPIC [8], an image classification framework based on MPC using SVM et al. Although MPC frameworks already support secure inference, the practical application of machine learning as a service in industrial cases needs to be supplemented. The instantiation of this work improves the automation and detection level of diamond synthesis, while protecting data security of both researchers and practitioners.  
Synthetic diamonds are synthesized by chemical vapor deposition (CVD) [9]. CVD is a process where carbon atoms are deposited from carbon gases under the action of high temperature plasma by a chemical reaction occurring on or in the vicinity of a heated substrate surface. In the process of producing synthetic diamonds, the surface quality inspection of substrate material is relying on experience of experts. The surface quality of the single crystal substrate will be the most important factor, which influences the quality of the epitaxial growth. Therefore, the quality of the synthetic diamonds is determined by the surface quality of the single crystal substrate. The images of the substrate are used to judge the growth quality of diamond. Identification of diamond substrate requires a high degree of knowledge reserve and experience, and the detection process is complex, which will lead to low efficiency of detection. However, the research on diamond substrate cultivation and defect detection technology is relatively exclusive, such as due to the protection of diamond substrate images and labels, commercial partners are not expected to get any data of diamond substrate, which seriously hinders the application of the research progress in the field of computer vision to the diamond defect detection task. At the same time, companies with advanced detection technology do not want their models to be leaked. Therefore, it is of great application value to use the MPC to realize the diamond substrate defect detection under the premise of protecting the data privacy of each party, as Figure 1.