A method for privacy-preserving collaborative filtering recommendations

With the continuous growth of the Internet and the progress of electronic commerce the issues of product recommendation and privacy protection are becoming increasingly important. Recommender Systems aim to solve the information overload problem by providing accurate recommendations of items to users. Collaborative filtering is considered the most widely used recommendation method for providing recommendations of items or users to other users in online environments. Additionally, collaborative filtering methods can be used with a trust network, thus delivering to the user recommendations from both a database of ratings and from users who the person who made the request knows and trusts. On the other hand, the users are having privacy concerns and are not willing to submit the required information (e.g., ratings for products), thus making the recommender system unusable. In this paper, we propose (a) an approach to product recommendation that is based on collaborative filtering and uses a combination of a ratings network with a trust network of the user to provide recommendations and (b) “neighbourhood privacy” that employs a modified privacy-aware role-based access control model that can be applied to databases that utilize recommender systems. Our proposed approach (1) protects user privacy with a small decrease in the accuracy of the recommendations and (2) uses information from the trust network to increase the accuracy of the recommendations, while, (3) providing privacy-preserving recommendations, as accurate as the recommendations provided without the privacy-preserving approach or the method that increased the accuracy applied

Authentication and authorisation in entrusted unions

This paper reports on the status of a project whose aim is to implement and demonstrate in a real-life environment an integrated eAuthentication and eAuthorisation framework to enable trusted collaborations and delivery of services across different organisational/governmental jurisdictions. This aim will be achieved by designing a framework with assurance of claims, trust indicators, policy enforcement mechanisms and processing under encryption to address the security and confidentiality requirements of large distributed infrastructures. The framework supports collaborative secure distributed storage, secure data processing and management in both the cloud and offline scenarios and is intended to be deployed and tested in two pilot studies in two different domains, viz, Bio-security incident management and Ambient Assisted Living (eHealth). Interim results in terms of security requirements, privacy preserving authentication, and authorisation are reported

Trust realisation in multi-domain collaborative environments

In the Internet-age, the geographical boundaries that have previously impinged upon inter-organisational collaborations have become decreasingly important. Of more importance for such collaborations is the notion and subsequent nature of trust - this is especially so in Grid-like environments where resources are both made available and subsequently accessed and used by remote users from a multitude of institutions with a variety of different privileges spanning across the collaborating resources. In this context, the ability to dynamically negotiate and subsequently enforce security policies driven by various levels of inter-organisational trust is essential. In this paper we present a dynamic trust negotiation (DTN) model and associated prototype implementation showing the benefits and limitations DTN incurs in supporting n-tier delegation hops needed for trust realisation in multi-domain collaborative environments

A collective intelligence approach for building student's trustworthiness profile in online learning

(c) 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.Information and communication technologies have been widely adopted in most of educational institutions to support e-Learning through different learning methodologies such as computer supported collaborative learning, which has become one of the most influencing learning paradigms. In this context, e-Learning stakeholders, are increasingly demanding new requirements, among them, information security is considered as a critical factor involved in on-line collaborative processes. Information security determines the accurate development of learning activities, especially when a group of students carries out on-line assessment, which conducts to grades or certificates, in these cases, IS is an essential issue that has to be considered. To date, even most advances security technological solutions have drawbacks that impede the development of overall security e-Learning frameworks. For this reason, this paper suggests enhancing technological security models with functional approaches, namely, we propose a functional security model based on trustworthiness and collective intelligence. Both of these topics are closely related to on-line collaborative learning and on-line assessment models. Therefore, the main goal of this paper is to discover how security can be enhanced with trustworthiness in an on-line collaborative learning scenario through the study of the collective intelligence processes that occur on on-line assessment activities. To this end, a peer-to-peer public student's profile model, based on trustworthiness is proposed, and the main collective intelligence processes involved in the collaborative on-line assessments activities, are presented.Peer ReviewedPostprint (author's final draft