Database indexing is a well studied problem. However,the advent of Hypertext databases opens new questions in indexing. Searches are often demarcated by pointers between text items. Thus the scope of the search may change dynamically, whereas traditional indexes cover a statically defined region such as a relation. We present techniques for indexing in hypertext databases and compare their performance.

In this paper a Document Base Management System is proposed that incorporates conventional database and hypertext ideas into a document database.  The Document Base operates as a server, users access the database through different application programs.  the query language which applications use to retrieve documents is described.

The first step in interoperating among multidatabases is semantic integration: Producing attribute correspondences that describe relationships between attributes or classes in different database schemas. Dynamic integration requires the ability to automatically extract database semantics, express them as metadata, and match semantically equivalent data elements to produce attribute correspondences. This process cannot be pre-programmed since the information to be accessed is heterogeneous. An architecture supporting dynamic integration is presented. Semint, a tool for automated semantic integration, that helps database administrators generate attribute correspondences, is discussed. A novel framework for dynamic integration and a query language for multidatabase systems that uses Semint as part of a complete semantic integration service are introduced. The framework supports dynamic integration as well as incremental integration. The advantages of the framework in an environment where full integration is not desired or complete knowledge of the databases to be integrated is unavailable are shown.

The secure multi-party computation (SMC) model provides means for balancing the use and confidentiality of distributed data. Increasing security concerns have led to a surge in work on practical secure multi- party computation protocols. However, most are only proven secure under the semi-honest model, and security under this adversary model is insufficient for most applications. In this paper, we propose a novel framework: accountable computing (AC) framework, which is sufficient or practical for many applications without the complexity and cost of a SMC-protocol under the malicious model. Furthermore, to show the applicability of the AC-framework, we present an application under this framework regarding privacy-preserving mining frequent itemsets.

Although the benefits of information sharing between supply-chain partners are well known, many compa-  nies are averse to share their “private” information due to fear of adverse impact of information leakage.  This paper uses techniques from Secure Multiparty Computation (SMC) to develop “secure protocols” for the CPFR r

We study data integrity verification in peer-to-peer media streaming for content distribution. Challenges include the timing constraint of streaming as well as the untrustworthiness of peers. We show the inade-  quacy of existing data integrity verification protocols, and propose Block-Oriented Probabilistic Verification (BOPV), an efficient protocol utilizing message digest and probabilistic verification. We then propose Tree-  based Forward Digest Protocol (TFDP) to further reduce the communication overhead. A comprehensive comparison is presented by comparing the performance of existing protocols and our protocols, with respect to overhead, security assurance level, and packet loss tolerance. Finally, experimental results are presented to evaluate the performance of our protocols.

Data mining introduces new problems in database security. The basic problem of using non-sensitive data to infer sensitive data is made more difficult by the “probabilistic” inferences possible with data mining. This paper shows how lower bounds from pattern recognition theory can be used to determine sample sizes where data mining tools cannot obtain reliable results.

There has been concern over the apparent conflict between privacy and data mining. There is no inherent conflict, as most types of data mining produce summary results that do not reveal information about individuals. The process of data mining may use private data, leading to the potential for privacy breaches. Secure Multiparty Computation shows that results can be produced without revealing the data used to generate them. The problem is that general techniques for secure multiparty computation do not scale to data-mining size computations. This paper presents an efficient protocol for securely determining the size of set intersection, and shows how this can be used to generate association rules where multiple parties have different (and private) information about the same set of individuals.