Discover the Secrets of Secure Data Provenance and Inference Control with Semantic Web!

In today's digital age, data has become a valuable asset for businesses, governments, and individuals alike. However, with the increased reliance on data comes the need for robust security measures to protect sensitive information from unauthorized access and manipulation. This is where the concept of secure data provenance and inference control with Semantic Web comes into play.

Semantic Web, also known as Web 3.0, is an extension of the World Wide Web that aims to make data more accessible and understandable by machines. It allows computers to interpret and process information intelligently, enabling better data management and analysis. By leveraging Semantic Web technologies, organizations can not only enhance their data governance practices but also ensure the security and integrity of their data.

So, what exactly is data provenance, and why is it important? Data provenance refers to the origin and history of a piece of data. It provides a detailed record of the processes and transformations that a data item has undergone throughout its lifecycle. In other words, it answers the fundamental question of "where did the data come from?".

Secure Data Provenance and Inference Control with Semantic Web

by J. Paul Guyer(1st Edition, Kindle Edition)

5 out of 5

Language	:	English
File size	:	33814 KB
Screen Reader	:	Supported
Print length	:	478 pages

FREE

Understanding the provenance of data is crucial for various reasons. For instance, in scientific research, it is essential to trace the origin of experimental data to verify the reliability and reproducibility of results. In the context of cybersecurity, knowing the provenance of data can help identify the source of an attack or detect data tampering attempts. Additionally, data provenance plays an important role in compliance with data protection regulations such as the General Data Protection Regulation (GDPR).

Now, let's delve into the concept of inference control. Inference control refers to mechanisms that protect sensitive information from being inferred or deduced by unauthorized entities. It aims to prevent the disclosure of confidential data through the analysis of non-confidential information. In other words, it ensures that sensitive information remains protected even when indirect knowledge can be derived from other publicly available data.

Semantic Web provides a framework for incorporating inference control mechanisms into data management systems. By leveraging ontologies and semantic annotations, it becomes possible to define fine-grained access control policies that govern the inference process. These policies specify the conditions under which certain information can be inferred or withheld, thus achieving a balance between data usefulness and data security.

One of the key technologies enabling secure data provenance and inference control with Semantic Web is the use of semantic annotations and metadata. Metadata provides additional information about the data, such as its origin, author, timestamp, and access permissions. By enriching data with metadata, organizations can establish trust, improve data integration, and enhance security and privacy controls.

Furthermore, semantic annotations enable the automatic discovery and integration of data from various sources. They allow machines to understand the semantics of data and make intelligent decisions based on that understanding. This capability is particularly valuable in scenarios where data from multiple sources needs to be combined and analyzed to derive meaningful insights without compromising privacy or security.

To illustrate the practical application of secure data provenance and inference control, let's consider an example. Imagine a financial institution that needs to share financial data with regulatory authorities for compliance purposes. By leveraging Semantic Web technologies, the institution can ensure that the shared data is accompanied by detailed provenance information, including the source, transformations, and access control policies. This approach enables auditors to verify the accuracy and integrity of the data while maintaining confidentiality of sensitive information.

, secure data provenance and inference control with Semantic Web provide organizations with powerful tools to better manage and protect their valuable data assets. By leveraging Semantic Web technologies, organizations can enhance data governance practices, ensure compliance with regulations, and mitigate data security risks. The ability to trace the origin of data and control the inferences made from it is crucial in today's data-driven world. So embrace the Semantic Web and unlock the full potential of your data!

Secure Data Provenance and Inference Control with Semantic Web

by J. Paul Guyer(1st Edition, Kindle Edition)

5 out of 5

Language	:	English
File size	:	33814 KB
Screen Reader	:	Supported
Print length	:	478 pages

FREE

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.