Human activity recognition (HAR) generates a massive amount of the dataset from the Internet of Things (IoT) devices, to enable multiple data providers to jointly produce predictive models for medical diagnosis. That the accuracy of the models is greatly improved when trained on a large number of datasets from these data providers on the untrusted cloud server is very significant and raises privacy concerns. With the migration of a deep neural network (DNN) in the learning experience in HAR, we present a privacy-preserving DNN model known as Multi-Scheme Differential Privacy (MSDP) depending on the fusion of Secure Multi-party Computation (SMC) and 𝜖-differential privacy, making it very practical since existing proposals are unable to make all the fully homomorphic encryption multi-key which is very impracticable. MSDP inputs a secure multi-party alternative to the ReLU function to reduce the communication and computational cost at a minimal level. With the aid of experimental verification on the four of the most widely used human activity recognition datasets, MSDP demonstrates superior performance with very good generalization performance and is proven to be secure as compared with existing ultramodern models without breach of privacy.

人类活动识别（HAR）从物联网（IoT）设备生成大量数据集，以使多个数据提供者可以共同生成用于医学诊断的预测模型。当在不受信任的云服务器上对来自这些数据提供者的大量数据集进行训练时，模型的准确性得到了极大的提高，这引起了人们的极大关注，并引发了隐私问题。随着深度神经网络（DNN）在HAR学习体验中的迁移，我们提出了一种安全的DNN模型，称为多方案差分隐私（MSDP），具体取决于安全多方计算（SMC）和𝜖-差异性隐私，使其非常实用，因为现有建议无法将所有完全同态的加密多密钥都制成，这是非常不切实际的。MSDP为ReLU功能输入了安全的多方替代方案，以在最小程度上降低通信和计算成本。借助对四个最广泛使用的人类活动识别数据集的实验验证，MSDP表现出了卓越的性能以及非常好的泛化性能，并且与现有的超现代模型相比，在不违反隐私的前提下，它是安全的。