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.
, Tyrone Cadenhead
, Murat Kantarcioglu
, Vaibhav Khadilkar
Country of Publication:
30 August 2019
Professional and scholarly
Further / Higher Education
Introduction. Supporting Technologies. Security and Provenance. Access Control and Semantic Web. The Inference Problem. Inference Engines. Inferencing Examples. Cloud Computing Tools and Frameworks. Section II Secure Data Provenance. Scalable and Efficient RBAC for Provenance. A Language for Provenance Access Control. Transforming Provenance Using Redaction. Section III Inference Control. Architecture for an Inference Controller. Inference Controller Design. Provenance Data Representation for Inference Control. Queries with Regular Path Expressions. Inference Control through Query Modification. Inference and Provenance. Implementing the Inference Controller. Section IV Unifying Framework. Risk and Inference Control. Novel Approaches to Handle the Inference Problem. A Cloud-Based Policy Manager for Assured Information Sharing. Security and Privacy with Respect to Inference. Big Data Analytics and Inference Control. Unifying Framework. Summary and Directions. Appendices: Data Management Systems, Developments, and Trends. Database Management and Security.
Thuraisingham, Bhavani; Cadenhead, Tyrone; Kantarcioglu, Murat; Khadilkar, Vaibhav