This SpringerBrief mainly focuses on effective big data analytics for CPS, and addresses the privacy issues that arise on various CPS applications. The authors develop a series of privacy preserving data analytic and processing methodologies through data driven optimization based on applied cryptographic techniques and differential privacy in this brief. This brief also focuses on effectively integrating the data analysis and data privacy preservation techniques to provide the most desirable solutions for the state-of-the-art CPS with various application-specific requirements. Cyber-physical systems (CPS) are the “next generation of engineered systems,” that integrate computation and networking capabilities to monitor and control entities in the physical world. Multiple domains of CPS typically collect huge amounts of data and rely on it for decision making, where the data may include individual or sensitive information, for e.g., smart metering, intelligent transportation, healthcare, sensor/data aggregation, crowd sensing etc. This brief assists users working in these areas and contributes to the literature by addressing data privacy concerns during collection, computation or big data analysis in these large scale systems. Data breaches result in undesirable loss of privacy for the participants and for the entire system, therefore identifying the vulnerabilities and developing tools to mitigate such concerns is crucial to build high confidence CPS. This Springerbrief targets professors, professionals and research scientists working in Wireless Communications, Networking, Cyber-Physical Systems and Data Science. Undergraduate and graduate-level students interested in Privacy Preservation of state-of-the-art Wireless Networks and Cyber-Physical Systems will use this Springerbrief as a study guide.

Smart manufacturing environments are revolutionizing the industrial sector by integrating advanced technologies, such as the Internet of Things (IoT), artificial intelligence (AI), and robotics, to achieve higher levels of efficiency, productivity, and safety. However, the increasing complexity and interconnectedness of these systems also introduce new security challenges that must be addressed to ensure the safety of human workers and the integrity of manufacturing processes. Key topics include risk assessment methodologies, secure communication protocols, and the development of standard specifications to guide the design and implementation of HCPS. Recent research highlights the importance of adopting a multi-layered approach to security, encompassing physical, network, and application layers. Furthermore, the integration of AI and machine learning techniques enables real-time monitoring and analysis of system vulnerabilities, as well as the development of adaptive security measures. Artificial Intelligence Solutions for Cyber-Physical Systems discusses such best practices and frameworks as NIST Cybersecurity Framework, ISO/IEC 27001, and IEC 62443 of advanced technologies. It presents strategies and methods to mitigate risks and enhance security, including cybersecurity frameworks, secure communication protocols, and access control measures. The book also focuses on the design, implementation, and management of secure HCPS in smart manufacturing environments. It covers a wide range of topics, including risk assessment, security architecture, data privacy, and standard specifications, for HCPS. The book highlights the importance of securing communication protocols, the role of artificial intelligence and machine learning in threat detection and mitigation, and the need for robust cybersecurity frameworks in the context of smart manufacturing.

Cybersecurity and Privacy in Cyber-Physical Systems collects and reports on recent high-quality research that addresses different problems related to cybersecurity and privacy in cyber-physical systems (CPSs). It Presents high-quality contributions addressing related theoretical and practical aspects Improves the reader’s awareness of cybersecurity and privacy in CPSs Analyzes and presents the state of the art of CPSs, cybersecurity, and related technologies and methodologies Highlights and discusses recent developments and emerging trends in cybersecurity and privacy in CPSs Proposes new models, practical solutions, and technological advances related to cybersecurity and privacy in CPSs Discusses new cybersecurity and privacy models, prototypes, and protocols for CPSs This comprehensive book promotes high-quality research by bringing together researchers and experts in CPS security and privacy from around the world to share their knowledge of the different aspects of CPS security. Cybersecurity and Privacy in Cyber-Physical Systems is ideally suited for policymakers, industrial engineers, researchers, academics, and professionals seeking a thorough understanding of the principles of cybersecurity and privacy in CPSs. They will learn about promising solutions to these research problems and identify unresolved and challenging problems for their own research. Readers will also have an overview of CPS cybersecurity and privacy design.

This book focuses on privacy and security concerns in big data and differentiates between privacy and security and privacy requirements in big data. It focuses on the results obtained after applying a systematic mapping study and implementation of security in the big data for utilizing in business under the establishment of “Business Intelligence”. The chapters start with the definition of big data, discussions why security is used in business infrastructure and how the security can be improved. In this book, some of the data security and data protection techniques are focused and it presents the challenges and suggestions to meet the requirements of computing, communication and storage capabilities for data mining and analytics applications with large aggregate data in business.

This book addresses the issues with privacy and security in Internet of things (IoT) networks which are susceptible to cyber-attacks and proposes deep learning-based approaches using artificial neural networks models to achieve a safer and more secured IoT environment. Due to the inadequacy of existing solutions to cover the entire IoT network security spectrum, the book utilizes artificial neural network models, which are used to classify, recognize, and model complex data including images, voice, and text, to enhance the level of security and privacy of IoT. This is applied to several IoT applications which include wireless sensor networks (WSN), meter reading transmission in smart grid, vehicular ad hoc networks (VANET), industrial IoT and connected networks. The book serves as a reference for researchers, academics, and network engineers who want to develop enhanced security and privacy features in the design of IoT systems.

This book aims to sort out the clear logic of the development of machine learning-driven privacy preservation in IoTs, including the advantages and disadvantages, as well as the future directions in this under-explored domain. In big data era, an increasingly massive volume of data is generated and transmitted in Internet of Things (IoTs), which poses great threats to privacy protection. Motivated by this, an emerging research topic, machine learning-driven privacy preservation, is fast booming to address various and diverse demands of IoTs. However, there is no existing literature discussion on this topic in a systematically manner. The issues of existing privacy protection methods (differential privacy, clustering, anonymity, etc.) for IoTs, such as low data utility, high communication overload, and unbalanced trade-off, are identified to the necessity of machine learning-driven privacy preservation. Besides, the leading and emerging attacks pose further threats to privacy protection in this scenario. To mitigate the negative impact, machine learning-driven privacy preservation methods for IoTs are discussed in detail on both the advantages and flaws, which is followed by potentially promising research directions. Readers may trace timely contributions on machine learning-driven privacy preservation in IoTs. The advances cover different applications, such as cyber-physical systems, fog computing, and location-based services. This book will be of interest to forthcoming scientists, policymakers, researchers, and postgraduates.