Privacy-preserving deep learning techniques for wearable sensor-based big data applications

Abstract

Wearable technologies have the potential to become a valuable influence on human daily life where they may enable observing the world in new ways, including, for example, using augmented reality (AR) applications. Wearable technology uses electronic devices that may be carried as accessories, clothes, or even embedded in the user's body. Although the potential benefits of smart wearables are numerous, their extensive and continual usage creates several privacy concerns and tricky information security challenges. In this paper, we present a comprehensive survey of recent privacy-preserving big data analytics applications based on wearable sensors. We highlight the fundamental features of security and privacy for wearable device applications. Then, we examine the utilization of deep learning algorithms with cryptography and determine their usability for wearable sensors. We also present a case study on privacy-preserving machine learning techniques. Herein, we theoretically and empirically evaluate the privacy-preserving deep learning framework's performance. We explain the implementation details of a case study of a secure prediction service using the convolutional neural network (CNN) model and the Cheon-Kim-Kim-Song (CHKS) homomorphic encryption algorithm. Finally, we explore the obstacles and gaps in the deployment of practical real-world applications. Following a comprehensive overview, we identify the most important obstacles that must be overcome and discuss some interesting future research directions.