Administering the Semantic Web: Confidentiality, Privacy and Trust Management

Administering the Semantic Web: Confidentiality, Privacy and Trust Management

Bhavani Thuraisingham (University of Texas at Dallas, USA), Natasha Tsybulnik (University of Texas at Dallas, USA) and Ashraful Alam (University of Texas at Dallas, USA)
DOI: 10.4018/978-1-60566-210-7.ch017
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Abstract

The Semantic Web is essentially a collection of technologies to support machine understandable Web pages as well as Information Interoperability. There has been much progress made on the Semantic Web including standards for eXtensible Markup Language, Resource Description Framework and Onotlogies. However, administration policies and techniques for enforcing them have received little attention. These policies include policies for security, privacy, data quality, integrity, trust and timely information processing. This chapter discusses administration policies for the Semantic Web as well as techniques for enforcing them. In particular, the authors will discuss an approach for ensuring confidentiality, privacy and trust for the Semantic Web. We will also discuss the inference and privacy problems within the context of administration policies.
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Trust, Privacy, And Confidentiality

In this section we will discuss issues on confidentiality, privacy and trust.

Definitions

Confidentiality, privacy, trust, integrity, and availability will be briefly defined with an examination of how these issues specifically relate to the trust management and inference problem. Confidentiality is preventing the release of unauthorized information. Privacy is a subset of confidentiality in that it is the prevention of unauthorized information from being released in regards to an individual. Integrity of data is the prevention of any modifications made by an unauthorized entity. Availability is the prevention of unauthorized omission of data. Trust is a measure of confidence in data correctness and legitimacy from a particular source.

Integrity, availability, and trust are all very closely related in the sense that data quality is of particular importance and all require individuals or entities processing and sending information to not alter the data in an unauthorized manner. If all of these issues, confidentiality, privacy, trust, integrity, and availability, are guaranteed, a system can be considered secure. Thus if the inference problem can be solved such that unauthorized information is not released, the rules of confidentiality, privacy, and trust will not be broken. A technique such as inference can either be used to aid or impair the cause of integrity, availability, and trust. If correctly used, inference can be used to infer trust management policies. Thus inference can be used for good or bad purposes. The intention is to prevent inferred unauthorized conclusions and to use inference to apply trust management.

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