Semantic Search over Encrypted Data in Cloud Computing

Cloud storage becomes more and more popular in the recent trend since it provides various benefits over the traditional storage solutions. Along with many benefits provided by cloud storage, many security problems arise in cloud storage which prevents enterprises from migrate their data to cloud storage. These security problems induce the data owners to encrypt all their sensitive data such as social security number (SSN), credit card information, and personal tax information before they can be stored in cloud storage. The encryption approach may have strengthened the data security of cloud data, but it degrades the data efficiency because the encryption reduces the searchability of the data. Many schemes were proposed in recent researches which enable keyword search over encrypted data in cloud computing, and these schemes contain weaknesses which make them impractical when applying these schemes in real-life scenarios. In this project, we developed a system to support semantic search over encrypted data in cloud computing with three different schemes. The three schemes that we developed are “Synonym-Based Keyword Search (SBKS)”, “Wikipedia-Based Keyword Search (WBKS)”, and “Wikipedia-Based Synonym Keyword Search (WBSKS)”. Based on our experiment data, it demonstrated that the indexes created by our schemes are 95% smaller and reduced the average search time by 95% if compared to the schemes proposed previously. These improvements illustrated that our developed schemes are more practical than the former proposed schemes

Reasoning & Querying – State of the Art

Various query languages for Web and Semantic Web data, both for practical use and as an area of research in the scientific community, have emerged in recent years. At the same time, the broad adoption of the internet where keyword search is used in many applications, e.g. search engines, has familiarized casual users with using keyword queries to retrieve information on the internet. Unlike this easy-to-use querying, traditional query languages require knowledge of the language itself as well as of the data to be queried. Keyword-based query languages for XML and RDF bridge the gap between the two, aiming at enabling simple querying of semi-structured data, which is relevant e.g. in the context of the emerging Semantic Web. This article presents an overview of the field of keyword querying for XML and RDF

On the Foundations of Data Interoperability and Semantic Search on the Web

This dissertation studies the problem of facilitating semantic search across disparate ontologies that are developed by different organizations. There is tremendous potential in enabling users to search independent ontologies and discover knowledge in a serendipitous fashion, i.e., often completely unintended by the developers of the ontologies. The main difficulty with such search is that users generally do not have any control over the naming conventions and content of the ontologies. Thus terms must be appropriately mapped across ontologies based on their meaning. The meaning-based search of data is referred to as semantic search, and its facilitation (aka semantic interoperability) then requires mapping between ontologies. In relational databases, searching across organizational boundaries currently involves the difficult task of setting up a rigid information integration system. Linked Data representations more flexibly tackle the problem of searching across organizational boundaries on the Web. However, there exists no consensus on how ontology mapping should be performed for this scenario, and the problem is open. We lay out the foundations of semantic search on the Web of Data by comparing it to keyword search in the relational model and by providing effective mechanisms to facilitate data interoperability across organizational boundaries. We identify two sharply distinct goals for ontology mapping based on real-world use cases. These goals are: (i) ontology development, and (ii) facilitating interoperability. We systematically analyze these goals, side-by-side, and contrast them. Our analysis demonstrates the implications of the goals on how to perform ontology mapping and how to represent the mappings. We rigorously compare facilitating interoperability between ontologies to information integration in databases. Based on the comparison, class matching is emphasized as a critical part of facilitating interoperability. For class matching, various class similarity metrics are formalized and an algorithm that utilizes these metrics is designed. We also experimentally evaluate the effectiveness of the class similarity metrics on real-world ontologies. In order to encode the correspondences between ontologies for interoperability, we develop a novel W3C-compliant representation, named skeleton