Secure Data Provenance and Inference Control with Semantic Web

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.

Table of Contents

Chapter 1: Introduction
Chapter 2: Security and Provenance
Chapter 3: Access Control and Semantic Web
Chapter 4: The Inference Problem
Chapter 5: Inference Engines
Chapter 6: Inferencing Examples
Chapter 7: Cloud Computing Tools and Frameworks
Chapter 8: Scalable and Efficient RBAC for Provenance
Chapter 9: A Language for Provenance Access Control
Chapter 10: Transforming Provenance Using Redaction
Chapter 11: Architecture for an Inference Controller
Chapter 12: Inference Controller Design
Chapter 13: Provenance Data Representation for Inference Control
Chapter 14: Queries with Regular Path Expressions
Chapter 15: Inference Control through Query Modification
Chapter 16: Inference and Provenance
Chapter 17: Implementing the Inference Controller
Chapter 18: Risk and Inference Control
Chapter 19: Novel Approaches to Handle the Inference Problem
Chapter 20: A Cloud-Based Policy Manager for Assured Information Sharing
Chapter 21: Security and Privacy with Respect to Inference
Chapter 22: Big Data Analytics and Inference Control
Chapter 23: Unifying Framework
Chapter 24: Summary and Directions

Appendix A: Data Management Systems, Developments, and Trends
Appendix B: Database Management and Security
Appendix C: A Perspective of the Inference Problem
Appendix D: Design and Implementation of a Database Inference Controller