Privacy-Preserving Deep Learning for Enabling Big Edge Data Analytics in Internet of Things
Mnigming Guo, Niki Pissinou, S. S. Iyengar · 2019
Big data analytics are pervasive in data-intensive 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 privacy-preserving manner. To make this happen, we first examine distributed learning techniques that are suitable for IoT architectures. We then propose a privacy-preserving 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.