Privacy in the Internet of Things is a fundamental challenge for the Ubiquitous healthcare systems that depend on the data aggregated and collaborative deep learning among different parties. This paper proposes the MSCryptoNet, a novel framework that enables the scalable execution and the conversion of the state-of-the-art learned neural network to the MSCryptoNet models in the privacy-preservation setting. We also design a method for approximation of the activation function basically used in the convolutional neural network (i.e., Sigmoid and Rectified linear unit) with low degree polynomials, which is vital for computations in the homomorphic encryption schemes. Our model seems to target the following scenarios: 1) the practical way to enforce the evaluation of classifier whose inputs are encrypted with possibly different encryption schemes or even different keys while securing all operations including intermediate results and 2) the minimization of the communication and computational cost of the data providers. The MSCryptoNet is based on the multi-scheme fully homomorphic encryption. We also prove that the MSCryptoNet as a privacy-preserving deep learning scheme over the aggregated encrypted data is secured.

This paper proposes a new dual horizontal squash capsule network (DHS-CapsNet) to classify the lung and colon cancers on histopathological images. DHS-CapsNet is made up of encoder feature fusion (EFF) and a novel horizontal squash (HSquash) function. The EFF aggregates the extracted feature from the 2-lane convolutional layers, which provides rich information for better accuracy. HSquash is proposed as a squash function to ensure that vectors are effectively squashed and produces sparsity for a high discriminative capsule to extract important information from images with varied backgrounds. To present the effectiveness of DHS-CapsNet empirically, we applied this method on histopathological images (LC25000 dataset). We achieved better results of 99.23% compared to traditional CapsNet (85.55%). The DHS-CapsNet provides the top-1 classification error of 0.77% compared to 14.45% of the traditional CapsNet. Our results illustrate that our method improves CapsNet and can be adopted as a computer-aided diagnostic method to support doctors in lung and colon cancer diagnostics.

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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.

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