MSDP: multi-scheme privacy-preserving deep learning via differential privacy

Abstract Human activity recognition (HAR) generates a massive amount of the dataset from Internet Things (IoT) devices, to enable multiple data providers jointly produce predictive models for medical diagnosis. That accuracy is greatly improved when trained on large number datasets these untrusted cloud server very significant and raises privacy concerns. With migration deep neural network (DNN) in learning experience HAR, we present privacy-preserving DNN model known as Multi-Scheme Differential Privacy (MSDP) depending fusion Secure Multi-party Computation (SMC)