Privacy Preserving Deep Learning with Distributed Encoders In this paper, we propose a distributed machine learning framework for training and inference in machine learning models using distributed data while preserving privacy of the data owner. In the training mode, we deploy an encoder on the enduser device which extracts high level features from input data. The extracted features along with the corresponding annotation are sent to a centralized machine learning server. In the inference mode, the users submit the extracted features from encoder instead of the original data for inference to the server. This approach enables users to contributed in training a machine learning model and use inference services without sharing their original data with the server or a third party. We have studied this approach on MNIST, Fashion, SVHN and CIFAR10 datasets. The results show high classification accuracy of neural networks, trained with encoded features, and high encryption performance of the encoders.

PrivacyPreserving Deep Learning for Enabling Big Edge Data Analytics in Internet of Things Big data analytics are pervasive in dataintensive systems and applications using machine learning and deep learning. IoT sensor devices are generating all types of big data, including structured (e.g., tables) and unstructured data (e.g., text and image), which are beyond the processing power of humans. However, how to conduct big data analysis on IoT data without compromising IoT privacy is still an open problem. IoT shall leverage powerful learning techniques to automatically learn patterns such as similarities, correlations and abnormalities from big sensing data in a privacypreserving manner. To make this happen, we first examine distributed learning techniques that are suitable for IoT architectures. We then propose a privacypreserving distributed learning framework with a novel dynamic deep learning mechanism to extract patterns and learn knowledge from IoT data. Simulations are performed to show the effectiveness and efficiency of our solution.