Abstract
As the Internet of Things (IoT) applications have been introduced into daily life, privacy issues have become significant concerns to users, network service providers, device producers, and related roles. This study provides a high-level introduction of current privacy-preserving solutions in IoT systems within the three phases of data collection, transmission, and storage. In these three phases, the following aspects were examined: (1). security protocols at the physical and data link layers; (2). network solutions; and (3). data storage and sharing approaches. Real-world implementations often involve more than one phase, and numerous technologies are combined to ensure privacy. Thus, an understanding of all phases and their technologies can be helpful for IoT research, design, development, and operation.
Highlights
This study provides a high-level introduction of current privacy-preserving solutions in Internet of Things (IoT) systems within the three phases of data collection, transmission, and storage
We propose a general solution for IoT networks by introducing a software-defined network (SDN) that enables a dynamic, programmable network configuration
For any IoT application, data are collected from the perception layer, transmitted across networks, and stored in a data storage
Summary
Privacy is a topic with a long history, appeared as early as in ancient Greek philosophical discussions. On top of the data security, privacy is related to the social context of the data (Parent, 1983), as data may contain personal information of an individual. By benefiting from the convergence of multiple technologies, such as cloud computing, artificial intelligence, fifth-generation (5G) networks, and software-defined networks, the IoT offers various applications covering many domains, including home care, healthcare, logistics, transport, and automated vehicular systems. It opens for a diversity of combinations and possibilities to leverage the connectivity to develop cohesive and optimally functional IoT applications and networks, leading to a considerable volume of personal data being generated, gathered, shared through networks, and subsequently analyzed.
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