Bhavani Thuraisingham – författare

Secure data science, which integrates cyber security and data science, is becoming one of the critical areas in both cyber security and data science. This is because the novel data science techniques being developed have applications in solving such cyber security problems as intrusion detection, malware analysis, and insider threat detection. However, the data science techniques being applied not only for cyber security but also for every application area—including healthcare, finance, manufacturing, and marketing—could be attacked by malware. Furthermore, due to the power of data science, it is now possible to infer highly private and sensitive information from public data, which could result in the violation of individual privacy. This is the first such book that provides a comprehensive overview of integrating both cyber security and data science and discusses both theory and practice in secure data science.

After an overview of security and privacy for big data services as well as cloud computing, this book describes applications of data science for cyber security applications. It also discusses such applications of data science as malware analysis and insider threat detection. Then this book addresses trends in adversarial machine learning and provides solutions to the attacks on the data science techniques. In particular, it discusses some emerging trends in carrying out trustworthy analytics so that the analytics techniques can be secured against malicious attacks. Then it focuses on the privacy threats due to the collection of massive amounts of data and potential solutions. Following a discussion on the integration of services computing, including cloud-based services for secure data science, it looks at applications of secure data science to information sharing and social media.

This book is a useful resource for researchers, software developers, educators, and managers who want to understand both the high level concepts and the technical details on the design and implementation of secure data science-based systems. It can also be used as a reference book for a graduate course in secure data science. Furthermore, this book provides numerous references that would be helpful for the reader to get more details about secure data science.

Secure data science, which integrates cyber security and data science, is becoming one of the critical areas in both cyber security and data science. This is because the novel data science techniques being developed have applications in solving such cyber security problems as intrusion detection, malware analysis, and insider threat detection. However, the data science techniques being applied not only for cyber security but also for every application area—including healthcare, finance, manufacturing, and marketing—could be attacked by malware. Furthermore, due to the power of data science, it is now possible to infer highly private and sensitive information from public data, which could result in the violation of individual privacy. This is the first such book that provides a comprehensive overview of integrating both cyber security and data science and discusses both theory and practice in secure data science.

After an overview of security and privacy for big data services as well as cloud computing, this book describes applications of data science for cyber security applications. It also discusses such applications of data science as malware analysis and insider threat detection. Then this book addresses trends in adversarial machine learning and provides solutions to the attacks on the data science techniques. In particular, it discusses some emerging trends in carrying out trustworthy analytics so that the analytics techniques can be secured against malicious attacks. Then it focuses on the privacy threats due to the collection of massive amounts of data and potential solutions. Following a discussion on the integration of services computing, including cloud-based services for secure data science, it looks at applications of secure data science to information sharing and social media.

This book is a useful resource for researchers, software developers, educators, and managers who want to understand both the high level concepts and the technical details on the design and implementation of secure data science-based systems. It can also be used as a reference book for a graduate course in secure data science. Furthermore, this book provides numerous references that would be helpful for the reader to get more details about secure data science.

Semantic Webs promise to revolutionize the way computers find and integrate data over the internet. They will allow Web agents to share and reuse data across applications, enterprises, and community boundaries. However, this improved accessibility poses a greater threat of unauthorized access, which could lead to the malicious corruption of information. Building Trustworthy Semantic Webs addresses the urgent demand for the development of effective mechanisms that will protect and secure semantic Webs. Design Flexible Security Policies to Improve EfficiencySecuring semantic Webs involves the formation of policies that will dictate what type of access Web agents are allowed. This text provides the tools needed to engineer these policies and secure individual components of the semantic Web, such as XML, RDF, and OWL. It also examines how to control unauthorized inferences on the semantic Web. Since this technology is not fully realized, the book emphasizes the importance of integrating security features into semantic Webs at the onset of their development. Through its expansive coverage, Building Trustworthy Semantic Webs describes how the creation of semantic security standards will ensure the dependability of semantic Webs. It provides Web developers with the tools they need to protect sensitive information and guarantee the success of semantic Web applications.

As the demand for data and information management continues to grow, so does the need to maintain and improve the security of databases, applications, and information systems. In order to effectively protect this data against evolving threats, an up-to-date understanding of the mechanisms for securing semantic Web technologies is essential.

Reviewing cutting-edge developments, Secure Semantic Service-Oriented Systems focuses on confidentiality, privacy, trust, and integrity management for Web services. It demonstrates the breadth and depth of applications of these technologies in multiple domains. The author lays the groundwork with discussions of concepts in trustworthy information systems and security for service-oriented architecture. Next, she covers secure Web services and applications—discussing how these technologies are used in secure interoperability, national defense, and medical applications.

Divided into five parts, the book describes the various aspects of secure service oriented information systems; including confidentiality, trust management, integrity, and data quality. It evaluates knowledge management and e-business concepts in services technologies, information management, semantic Web security, and service-oriented computing. You will also learn how it applies to Web services, service-oriented analysis and design, and specialized and semantic Web services.

The author covers security and design methods for service-oriented analysis, access control models for Web Services, identity management, access control and delegation, and confidentiality. She concludes by examining privacy, trust, and integrity, the relationship between secure semantic Web technologies and services, secure ontologies, and RDF. The book also provides specific consideration to activity management such as e-business, collaboration, healthcare, and finance.