Enforcing Trade Secrets among Competitors on the Semantic Web

In this thesis, we present a novel approach for the preservation of trade secrets in a Business-to-Business (B2B) environment that involves trade among competitors. The Web provides a low cost medium for B2B collaborations. Information exchange may take place during such a collaboration. The exchanged information may be of a sensitive nature, forming a business trade secret. The open nature of the Web calls for techniques to prevent the disclosure of trade secrets. The emerging Semantic Web is expected to make the challenge more acute in terms of trade secret protection due to the automation of B2B interactions. In this thesis, the different businesses are represented by Web services on the envisioned Semantic Web. We propose a Peer-to-Peer (P2P) approach for preserving trade secrets in B2B interactions. We introduce a set of techniques based on data perturbation for preserving data privacy. The techniques presented in our thesis are implemented in WebBIS, a prototype for accessing e-business Web services. Finally, we conduct an extensive performance study (analytical and experimental) of the proposed techniques.