Introductory Chapter: Data Privacy Preservation on the Internet of Things
Jaydip Sen, Subhasis Dasgupta · IntechOpen eBooks · 2023
Recent developments in hardware and information technology have enabled the emergence of billions of connected, intelligent devices around the world exchanging information with minimal human involvement.This paradigm, known as the Internet of Things (IoT), is progressing quickly, with an estimated 27 billion devices by 2025 (almost four devices per person) [1,2].These smart devices help improve our quality of life, with wearables to monitor health, vehicles that interact with traffic centers and other vehicles to ensure safety, and various home appliances offering comfort.This increase in the number of IoT devices and successful IoT services has generated tremendous data.The International Data Corporation report estimates that by 2025 this data will grow from 4 to 140 zettabytes [3].However, this humongous volume of data poses growing concerns for user privacy.Gartner predicts approximately 15 billion connected devices will be linked to computing networks by 2022 [4].These gadgets could be vulnerable, and the massive amounts of unsecured online data create a liability.In addition, users having difficulty controlling the data from their devices has highlighted privacy as a major issue.To guarantee high levels of user data protection, IoT systems must adhere to regulations such as the European Union's general data protection regulation (GDRP) of 2018 [5].GDPR is a law enacted in the European Union that specifies rules for how organiza tions and companies must use personal data without violating their integrity.These regulation policies focus on giving users control over what is collected, when, and for what purpose.By 2023, the regulators will demand organizations protect consumer privacy rights for more than 5 billion citizens and comply with more than 70% of the GDPR requirements [5].Traditional privacy protection schemes are insufficient for IoT applications which necessitate new techniques such as distributed cybersecurity controls, models, and decisions that take into account vulnerabilities in system development platforms as well as malicious users and attack surfaces.Machine learning techniques can provide improved detection of novel cyberattacks when dealing with large volumes of data in IoT systems.Furthermore, they can enhance how sensitive data are shared between components to keep them secure.Machine learning-based schemes thus improve the operations related to privacy protection and more