Open distributed environments, such as the World Wide Web, facilitate information sharing but provide limited support to the protection of sensitive information and resources. Trust negotiation (TN) frameworks have been proposed as a better solution for open environments, in which parties may get in touch and interact without being previously known to each other. In this paper, we illustrate Protune, a rule-based TN system. By describing Protune, we will illustrate the advantages that arise from an advanced rule-based approach in terms of deployment efforts, user friendliness, communication efficiency, and interoperability. The generality and technological feasibility of Protune's approach are assessed through an extensive analysis and experimental evaluations.

Privacy-preserving document exchange among collaboration groups in an enterprise as well as across enterprises requires techniques for sharing and search of access-controlled information through largely untrusted servers. In these settings search systems need to provide confidentiality guarantees for shared information while offering IR properties comparable to the ordinary search engines. Top-k is a standard IR technique which enables fast query execution on very large indexes and makes systems highly scalable. However, indexing access-controlled information for top-k retrieval is a challenging task due to the sensitivity of the term statistics used for ranking.

To carry out work assignments, small groups distributed within a larger enterprise often need to share documents among themselves while shielding those documents from others’ eyes. In this situation, users need an indexing facility that can quickly locate relevant documents that they are allowed to access, without (1) leaking information about the remaining documents, (2) imposing a large management burden as users, groups, and documents evolve, or (3) requiring users to agree on a central completely trusted authority. To address this problem, we propose the concept of r-confidentiality, which captures the degree of information leakage from an index about the terms contained in inaccessible documents. Then we propose the r-confidential Zerber indexing facility for sensitive documents, which uses secret splitting and term merging to provide tunable limits on information leakage, even under statistical attacks; requires only limited trust in a central indexing authority; and is extremely easy to use and administer. Experiments with real-world data show that Zerber offers excellent performance for index insertions and lookups while requiring only a modest amount of storage space and network bandwidth

Access to learning information is still restricted due to the lack of technical and semantic interoperability, locking knowledge resources in disconnected islands. This situation does not provide learners with a motivating environment in which to access and share learning information. In order to target emerging needs for lifelong competence development, a flexible and open source environment for management and sharing of knowledge resources must be built on top of an infrastructure that maximizes the amount of information available, therefore integrating centralised repositories and user desktop resources as well as emergent Web 2.0 applications. This paper describes the challenges and requirements that need to be addressed and presents a set of interfaces and our current implementation of the knowledge resource sharing and management service oriented architecture.

This case study summarizes the demonstration of a semantic network of interoperable educational systems referred to as Smart Space for Learningtrade. We started connecting several educational nodes in projects such as Elena, Prolearn, and Icamp. Integration was achieved by using the interaction standard SQI, common schemas for querying and results presentation, and query exchange language, e.g. QEL. The paper particularly focuses on how heterogeneous nodes can be made interoperable by reusing generalizations of mediating components - building blocks for a Smart Space for Learningtrade

Most of the current algorithms for finding related pages are exclusively based on text corpora of the WWW or incorporate only authority or hub values of pages. In this paper, we present HubFinder, a new fast algorithm for finding related pages exploring the link structure of the Web graph. Its criterion for filtering output pages is "pluggable", depending on the user's interests, and may vary from global page ranks to text content, etc. We also introduce HubRank, a new ranking algorithm which gives a more complete view of page "importance" by biasing the authority measure of PageRank towards hub values of pages. Finally, we present an evaluation of these algorithms in order to prove their qualities experimentally.