Secure Semantic Service Oriented Systems

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.

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. Reviewi

Based on the paradigm of model-driven security, the authors of this book show how to systematically design and realize security-critical applications for SOAs. In a second step, they apply the principles of model-driven security to SOAs.

With an ever-increasing amount of information on the web, it is critical to understand the pedigree, quality, and accuracy of your data. Using provenance, you can ascertain the quality of data based on its ancestral data and derivations, track back to sources of errors, allow automatic re-enactment of derivations to update data, and provide attribution of the data source. Secure Data Provenance and Inference Control with Semantic Web supplies step-by-step instructions on how to secure the provenance of your data to make sure it is safe from inference attacks. It details the design and implementation of a policy engine for provenance of data and presents case studies that illustrate solutions in a typical distributed health care system for hospitals. Although the case studies describe solutions in the health care domain, you can easily apply the methods presented in the book to a range of other domains. The book describes the design and implementation of a policy engine for provenance and demonstrates the use of Semantic Web technologies and cloud computing technologies to enhance the scalability of solutions. It covers Semantic Web technologies for the representation and reasoning of the provenance of the data and provides a unifying framework for securing provenance that can help to address the various criteria of your information systems. Illustrating key concepts and practical techniques, the book considers cloud computing technologies that can enhance the scalability of solutions. After reading this book you will be better prepared to keep up with the on-going development of the prototypes, products, tools, and standards for secure data management, secure Semantic Web, secure web services, and secure cloud computing.

This book constitutes the proceedings of the 4th International Conference on Network Security and Applications held in Chennai, India, in July 2011. The 63 revised full papers presented were carefully reviewed and selected from numerous submissions. The papers address all technical and practical aspects of security and its applications for wired and wireless networks and are organized in topical sections on network security and applications, ad hoc, sensor and ubiquitous computing, as well as peer-to-peer networks and trust management.

Analyzing and Securing Social Networks focuses on the two major technologies that have been developed for online social networks (OSNs): (i) data mining technologies for analyzing these networks and extracting useful information such as location, demographics, and sentiments of the participants of the network, and (ii) security and privacy technolo

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.

Although the use of cloud computing platforms and applications has expanded rapidly, most books on the subject focus on high-level concepts. There has long been a need for a book that provides detailed guidance on how to develop secure clouds.Filling this void, Developing and Securing the Cloud provides a comprehensive overview of cloud computing t