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Privacy protection within large databases can be a challenge. By examining the current problems and challenges this domain is facing, more efficient strategies can be established to safeguard personal information against invasive pressures. HCI Challenges and Privacy Preservation in Big Data Security is an informative scholarly publication that discusses how human-computer interaction impacts privacy and security in almost all sectors of modern life. Featuring relevant topics such as large scale security data, threat detection, big data encryption, and identity management, this reference source is ideal for academicians, researchers, advanced-level students, and engineers that are interested in staying current on the advancements and drawbacks of human-computer interaction within the world of big data.
The growth of innovative cyber threats, many based on metamorphosing techniques, has led to security breaches and the exposure of critical information in sites that were thought to be impenetrable. The consequences of these hacking actions were, inevitably, privacy violation, data corruption, or information leaking. Machine learning and data mining techniques have significant applications in the domains of privacy protection and cybersecurity, including intrusion detection, authentication, and website defacement detection, that can help to combat these breaches. Applications of Machine Learning and Deep Learning for Privacy and Cybersecurity provides machine and deep learning methods for analysis and characterization of events regarding privacy and anomaly detection as well as for establishing predictive models for cyber attacks or privacy violations. It provides case studies of the use of these techniques and discusses the expected future developments on privacy and cybersecurity applications. Covering topics such as behavior-based authentication, machine learning attacks, and privacy preservation, this book is a crucial resource for IT specialists, computer engineers, industry professionals, privacy specialists, security professionals, consultants, researchers, academicians, and students and educators of higher education.
In recent years, media digitalization has been booming. It has, however, led to a phenomenon of private data hacking, which was stimulated by the expansion of the data exchange system. These hackers are being countered by using new techniques, including cryptography and watermarking. Therefore, in the fields of information security, data encryption, and watermarking, there exists a need for a collection of original research in this area. Applications of Encryption and Watermarking for Information Security provides relevant theoretical frameworks and the latest empirical research findings in the domains of security and privacy. It is written for professionals who want to improve their understanding of the strategic role of trust at different levels of information security, that is, trust at the level of cryptography and watermarking and at the level of securing multimedia data. Covering topics such as background subtraction, moving object detection, and visual watermark identification, this premier reference source is an excellent resource for security and privacy professionals, IT managers, practitioners, students and educators of higher education, librarians, researchers, and academicians.
This book offers a collection of high-quality peer-reviewed research papers presented at the Second International Conference on Communication and Computational Technologies (ICCCT 2019), held at Rajasthan Institute of Engineering and Technology, Jaipur, Rajasthan, India, on 30–31 August 2019. In contributions prepared by researchers from academia and industry alike, the book discusses a wide variety of industrial, engineering and scientific applications of emerging techniques.
In today’s modern age of information, new technologies are quickly emerging and being deployed into the field of information technology. Cloud computing is a tool that has proven to be a versatile piece of software within IT. Unfortunately, the high usage of Cloud has raised many concerns related to privacy, security, and data protection that have prevented cloud computing solutions from becoming the prevalent alternative for mission critical systems. Up-to-date research and current techniques are needed to help solve these vulnerabilities in cloud computing. Modern Principles, Practices, and Algorithms for Cloud Security is a pivotal reference source that provides vital research on the application of privacy and security in cloud computing. While highlighting topics such as chaos theory, soft computing, and cloud forensics, this publication explores present techniques and methodologies, as well as current trends in cloud protection. This book is ideally designed for IT specialists, scientists, software developers, security analysts, computer engineers, academicians, researchers, and students seeking current research on the defense of cloud services.
Understanding cybersecurity principles and practices is vital to all users of IT systems and services, and is particularly relevant in an organizational setting where the lack of security awareness and compliance amongst staff is the root cause of many incidents and breaches. If these are to be addressed, there needs to be adequate support and provision for related training and education in order to ensure that staff know what is expected of them and have the necessary skills to follow through. Cybersecurity Education for Awareness and Compliance explores frameworks and models for teaching cybersecurity literacy in order to deliver effective training and compliance to organizational staff so that they have a clear understanding of what security education is, the elements required to achieve it, and the means by which to link it to the wider goal of good security behavior. Split across four thematic sections (considering the needs of users, organizations, academia, and the profession, respectively), the chapters will collectively identify and address the multiple perspectives from which action is required. This book is ideally designed for IT consultants and specialist staff including chief information security officers, managers, trainers, and organizations.
The notion of surveillance has become increasingly more crucial in public conversation as new tools of observation are obtained by many different players. The traditional notion of “overseeing” is being increasingly replaced by multi-level surveillance where many different actors, at different levels of hierarchy, from the child surveilling the parent to the state surveilling its citizens, are entering the surveillance theater. This creates a unique surveillance ecosystem where the individual is observed not only as an analog flesh-and-blood body moving through real spaces such as a shopping mall, but also tracked as a data point where the volume of data is perpetually and permanently expanding as the digital life story is inscribed in the digital spaces. The combined narrative of the individual is now under surveillance. Modern Day Surveillance Ecosystem and Impacts on Privacy navigates the reader through an understanding of the self as a narrative element that is open for observation and analysis. This book provides a broad-based and theoretically grounded look at the overall processes of surveillance in a global system. Covering topics including commodity, loss of privacy, and big data, this text is essential for researchers, government officials, policymakers, security analysts, lawmakers, teachers, professors, graduate and undergraduate students, practitioners, and academicians interested in communication, technology, surveillance, privacy, and more.
This book constitutes the refereed proceedings of the Software Engineering and Algorithms section of the 10th Computer Science On-line Conference 2021 (CSOC 2021), held on-line in April 2021. Software engineering research and its applications to intelligent algorithms take an essential role in computer science research. In this book, modern research methods, application of machine and statistical learning in the software engineering research are presented.
Cloud computing has experienced explosive growth and is expected to continue to rise in popularity as new services and applications become available. As with any new technology, security issues continue to be a concern, and developing effective methods to protect sensitive information and data on the cloud is imperative. Cloud Security: Concepts, Methodologies, Tools, and Applications explores the difficulties and challenges of securing user data and information on cloud platforms. It also examines the current approaches to cloud-based technologies and assesses the possibilities for future advancements in this field. Highlighting a range of topics such as cloud forensics, information privacy, and standardization and security in the cloud, this multi-volume book is ideally designed for IT specialists, web designers, computer engineers, software developers, academicians, researchers, and graduate-level students interested in cloud computing concepts and security.
With the recent growth of big data and the internet of things (IoT), individuals can now upload, retrieve, store, and collect massive amounts of information to help drive decisions and optimize processes. Due to this, a new age of predictive computing is taking place, and data can now be harnessed to predict unknown occurrences or probabilities based on data collected in real time. Predictive Intelligence Using Big Data and the Internet of Things highlights state-of-the-art research on predictive intelligence using big data, the IoT, and related areas to ensure quality assurance and compatible IoT systems. Featuring coverage on predictive application scenarios to discuss these breakthroughs in real-world settings and various methods, frameworks, algorithms, and security concerns for predictive intelligence, this book is ideally designed for academicians, researchers, advanced-level students, and technology developers.