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The Internet of Things (IoT) connects and shares data collected from smart devices in several domains, such as smart home, smart grid, and healthcare. According to Cisco, the number of connected devices is expected to reach 500 Billion by 2030. Five hundred zettabytes of data will be produced by tremendous machines and devices. Usually, these collected data are very sensitive and include metadata, such as location, time, and context. Their analysis allows the collector to deduce personal habits, behaviors and preferences of individuals. Besides, these collected data require the collaboration of several parties to be analyzed. Thus, due to the high level of IoT data sensitivity and lack of trust on the involved parties in the IoT environment, the collected data by different IoT devices should not be shared with each other, without enforcing data owner privacy. In fact, IoT data privacy has become a severe challenge nowadays, especially with the increasing legislation pressure. Our research focused on three complementary issues, mainly (i) the definition of a semantic layer designing the privacy requirements in the IoT domain, (ii) the IoT device monitoring and the enforcement of a privacy policy that matches both the data owner's privacy preferences and the data consumer's terms of service, and (iii) the establishment of an end-to-end privacy-preserving solution for IoT data in a decentralized architecture while eliminating the need to trust any involved IoT parties. To address these issues, our work contributes to three axes. First, we proposed a new European Legal compliant ontology for supporting preserving IoT PrivacY, called LIoPY that describes the IoT environment and the privacy requirements defined by privacy legislation and standards. Then, we defined a reasoning process whose goal is generating a privacy policy by matching between the data owner's privacy preferences and the data consumer's terms of service. This privacy policy specifies how the data will be handled once shared with a specific data consumer. In order to ensure this privacy policy enforcement, we introduced an IoT data privacy-preserving framework, called PrivBlockchain, in the second research axis. PrivBlockchain is an end-to-end privacy-preserving framework that involves several parties in the IoT environment for preserving IoT data privacy during the phases of collection, transmission, storage, and processing. The proposed framework relied on, on the one hand, the blockchain technology, thus supporting a decentralized architecture while eliminating the need to trust any involved IoT parties and, on the other hand, the smart contracts, thus supporting a machine-readable and self-enforcing privacy policy whose goal is to preserve the privacy during the whole data lifecycle, covering the collection, transmission, storage and processing phases. Finally, in the third axis, we designed and implemented the proposal in order to prove its feasibility and analyze its performances.
This book presents the proceedings of the 2020 2nd International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy (SPIoT-2021), online conference, on 30 October 2021. It provides comprehensive coverage of the latest advances and trends in information technology, science and engineering, addressing a number of broad themes, including novel machine learning and big data analytics methods for IoT security, data mining and statistical modelling for the secure IoT and machine learning-based security detecting protocols, which inspire the development of IoT security and privacy technologies. The contributions cover a wide range of topics: analytics and machine learning applications to IoT security; data-based metrics and risk assessment approaches for IoT; data confidentiality and privacy in IoT; and authentication and access control for data usage in IoT. Outlining promising future research directions, the book is a valuable resource for students, researchers and professionals and provides a useful reference guide for newcomers to the IoT security and privacy field.
This book presents the proceedings of the 2020 2nd International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy (SPIoT-2021), online conference, on 30 October 2021. It provides comprehensive coverage of the latest advances and trends in information technology, science and engineering, addressing a number of broad themes, including novel machine learning and big data analytics methods for IoT security, data mining and statistical modelling for the secure IoT and machine learning-based security detecting protocols, which inspire the development of IoT security and privacy technologies. The contributions cover a wide range of topics: analytics and machine learning applications to IoT security; data-based metrics and risk assessment approaches for IoT; data confidentiality and privacy in IoT; and authentication and access control for data usage in IoT. Outlining promising future research directions, the book is a valuable resource for students, researchers and professionals and provides a useful reference guide for newcomers to the IoT security and privacy field.
This book presents the proceedings of The 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy (SPIoT-2020), held in Shanghai, China, on November 6, 2020. Due to the COVID-19 outbreak problem, SPIoT-2020 conference was held online by Tencent Meeting. It provides comprehensive coverage of the latest advances and trends in information technology, science and engineering, addressing a number of broad themes, including novel machine learning and big data analytics methods for IoT security, data mining and statistical modelling for the secure IoT and machine learning-based security detecting protocols, which inspire the development of IoT security and privacy technologies. The contributions cover a wide range of topics: analytics and machine learning applications to IoT security; data-based metrics and risk assessment approaches for IoT; data confidentiality and privacy in IoT; and authentication and access control for data usage in IoT. Outlining promising future research directions, the book is a valuable resource for students, researchers and professionals and provides a useful reference guide for newcomers to the IoT security and privacy field.
This book presents the proceedings of The 2020 International Conference on Machine Learning and Big Data Analytics for IoT Security and Privacy (SPIoT-2020), held in Shanghai, China, on November 6, 2020. Due to the COVID-19 outbreak problem, SPIoT-2020 conference was held online by Tencent Meeting. It provides comprehensive coverage of the latest advances and trends in information technology, science and engineering, addressing a number of broad themes, including novel machine learning and big data analytics methods for IoT security, data mining and statistical modelling for the secure IoT and machine learning-based security detecting protocols, which inspire the development of IoT security and privacy technologies. The contributions cover a wide range of topics: analytics and machine learning applications to IoT security; data-based metrics and risk assessment approaches for IoT; data confidentiality and privacy in IoT; and authentication and access control for data usage in IoT. Outlining promising future research directions, the book is a valuable resource for students, researchers and professionals and provides a useful reference guide for newcomers to the IoT security and privacy field.
The integration of fog computing with the resource-limited Internet of Things (IoT) network formulates the concept of the fog-enabled IoT system. Due to a large number of IoT devices, the IoT is a main source of Big Data. A large volume of sensing data is generated by IoT systems such as smart cities and smart-grid applications. A fundamental research issue is how to provide a fast and efficient data analytics solution for fog-enabled IoT systems. Big Data Analytics in Fog-Enabled IoT Networks: Towards a Privacy and Security Perspective focuses on Big Data analytics in a fog-enabled-IoT system and provides a comprehensive collection of chapters that touch on different issues related to healthcare systems, cyber-threat detection, malware detection, and the security and privacy of IoT Big Data and IoT networks. This book also emphasizes and facilitates a greater understanding of various security and privacy approaches using advanced artificial intelligence and Big Data technologies such as machine and deep learning, federated learning, blockchain, and edge computing, as well as the countermeasures to overcome the vulnerabilities of the fog-enabled IoT system.
The Internet of Things (IoT) can be defined as any network of things capable of generating, storing and exchanging data, and in some cases acting on it. This new form of seamless connectivity has many applications: smart cities, smart grids for energy management, intelligent transport, environmental monitoring, healthcare systems, etc. and EU policymakers were quick to realize that machine-to-machine communication and the IoT were going to be vital to economic development. It was also clear that the security of such systems would be of paramount importance and, following the European Commission’s Cybersecurity Strategy of the European Union in 2013, the EU’s Horizon 2020 programme was set up to explore available options and possible approaches to addressing the security and privacy issues of the IoT. This book presents 10 papers which have emerged from the research of the Horizon 2020 and CHIST-ERA programmes, and which address a wide cross-section of projects ranging from the secure management of personal data and the specific challenges of the IoT with respect to the GDPR, through access control within a highly dynamic IoT environment and increasing trust with distributed ledger technologies, to new cryptographic approaches as a counter-measure for side-channel attacks and the vulnerabilities of IoT-based ambient assisted living systems. The security and safety of the Internet of Things will remain high on the agenda of policymakers for the foreseeable future, and this book provides an overview for all those with an interest in the field.
This book highlights state-of-the-art research on big data and the Internet of Things (IoT), along with related areas to ensure efficient and Internet-compatible IoT systems. It not only discusses big data security and privacy challenges, but also energy-efficient approaches to improving virtual machine placement in cloud computing environments. Big data and the Internet of Things (IoT) are ultimately two sides of the same coin, yet extracting, analyzing and managing IoT data poses a serious challenge. Accordingly, proper analytics infrastructures/platforms should be used to analyze IoT data. Information technology (IT) allows people to upload, retrieve, store and collect information, which ultimately forms big data. The use of big data analytics has grown tremendously in just the past few years. At the same time, the IoT has entered the public consciousness, sparking people’s imaginations as to what a fully connected world can offer. Further, the book discusses the analysis of real-time big data to derive actionable intelligence in enterprise applications in several domains, such as in industry and agriculture. It explores possible automated solutions in daily life, including structures for smart cities and automated home systems based on IoT technology, as well as health care systems that manage large amounts of data (big data) to improve clinical decisions. The book addresses the security and privacy of the IoT and big data technologies, while also revealing the impact of IoT technologies on several scenarios in smart cities design. Intended as a comprehensive introduction, it offers in-depth analysis and provides scientists, engineers and professionals the latest techniques, frameworks and strategies used in IoT and big data technologies.
This book presents a comprehensive framework for IoT, including its architectures, security, privacy, network communications, and protocols. The book starts by providing an overview of the aforementioned research topics, future directions and open challenges that face the IoT development. The authors then discuss the main architectures in the field, which include Three- and Five-Layer Architectures, Cloud and Fog Based Architectures, a Social IoT Application Architecture. In the security chapter, the authors outline threats and attacks, privacy preservation, trust and authentication, IoT data security, and social awareness. The final chapter presents case studies including smart home, wearables, connected cars, industrial Internet, smart cities, IoT in agriculture, smart retail, energy engagement, IoT in healthcare, and IoT in poultry and farming. Discusses ongoing research into the connection of the physical and virtual worlds; Includes the architecture, security, privacy, communications, and protocols of IoT; Presents a variety of case studies in IoT including wearables, smart cities, and energy management.
This book reviews research works in recent trends in blockchain, AI, and Digital Twin based IoT data analytics approaches for providing the privacy and security solutions for Fog-enabled IoT networks. Due to the large number of deployments of IoT devices, an IoT is the main source of data and a very high volume of sensing data is generated by IoT systems such as smart cities and smart grid applications. To provide a fast and efficient data analytics solution for Fog-enabled IoT systems is a fundamental research issue. For the deployment of the Fog-enabled-IoT system in different applications such as healthcare systems, smart cities and smart grid systems, security, and privacy of big IoT data and IoT networks are key issues. The current centralized IoT architecture is heavily restricted with various challenges such as single points of failure, data privacy, security, robustness, etc. This book emphasizes and facilitates a greater understanding of various security and privacy approaches using the advances in Digital Twin and Blockchain for data analysis using machine/deep learning, federated learning, edge computing and the countermeasures to overcome these vulnerabilities.