One is either on Facebook or not. Of course, this assessment is controversial and its rationale arguable. It is nevertheless not far, for many, from the reason behind joining social media and... Show More

One is either on Facebook or not. Of course, this assessment is controversial and its rationale arguable. It is nevertheless not far, for many, from the reason behind joining social media and publishing and sharing details of their professional and private lives. Not only the personal details that may be revealed, but also the structure of the networks are sources of invaluable information for any organization wanting to understand and learn about social groups, their dynamics and members. These organizations may or may not be benevolent. It is important to devise, design and evaluate solutions that guarantee some privacy. One approach that reconciles the different stakeholders’ requirement is the publication of a modified graph. The perturbation is hoped to be sufficient to protect members’ privacy while it maintains sufficient utility for analysts wanting to study the social media as a whole. In this paper, the authors try to empirically quantify the inevitable trade-off between utility and privacy. They do so for two state-of-the-art graph anonymization algorithms that protect against most structural attacks, the k-automorphism algorithm and the k-degree anonymity algorithm. The authors measure several metrics for a series of real graphs from various social media before and after their anonymization under various settings.

Data integration involves combining data residing in different sources and providing users with a unified view of these data through what is called a “global schema’’. The authors address here the... Show More

Data integration involves combining data residing in different sources and providing users with a unified view of these data through what is called a “global schema’’. The authors address here the problem of the construction of this global schema, with a minimum human effort, in the semantic Web context where data sources are annotated with ontologies. The authors aim to facilitate the task of building a common vocabulary (ontology) that will serve as a shared conceptual level for several heterogeneous data sources needing to share their data in a specific application domain. The authors propose a solution based on the use of a domain reference ontology (or “background knowledge’’) as a mediation support and some reasoning techniques in order to minimize human intervention. The work presented here is implemented as a prototype for Semi Automatic Global Ontology Building (SAGOB).

The development of new applications of electronic commerce (e-commerce) that require the payment of small amounts of money to purchase services or goods opens new challenges in the security and... Show More

The development of new applications of electronic commerce (e-commerce) that require the payment of small amounts of money to purchase services or goods opens new challenges in the security and privacy fields. These payments are called micropayments and they provide a trade-off between efficiency and security requirements to pay low-value items. It is usual to assume low value fraud to achieve efficiency in micropayment systems. In this paper the authors present an improved version of an efficient and secure micropayment scheme which fulfils the security properties that guarantee no financial risk for merchants and the privacy of the customers. In addition, the proposed system defines a fair exchange between the coin and the desired good or service. In this fair exchange, the anonymity and untraceability of the customers are assured. Moreover, customers can request a refund whether they are no more interested on the services offered by merchants. As a novelty, an improvement of the scheme avoids customers to fraudulently use a refund operation to gain a little amount of money (called coupon). Thus, a new resolution subprotocol allows the merchant to avoid the loss of any single coupon.

Tag clouds have become an effective tool to quickly perceive the most prominent terms embedded within textual data. Tag clouds help grasp the main theme of a corpus without exploring the pile of... Show More

Tag clouds have become an effective tool to quickly perceive the most prominent terms embedded within textual data. Tag clouds help grasp the main theme of a corpus without exploring the pile of documents. However, the effectiveness of tag clouds to conceptualize text corpora is directly proportional to the quality of the tags. In this paper, the authors propose a keyphrase-based tag cloud generation framework. In contrast to existing tag cloud generation systems that use single words as tags and their frequency counts to determine the font size of the tags, the proposed framework identifies feasible keyphrases and uses them as tags. The font-size of a keyphrase is determined as a function of its relevance weight. Instead of using partial or full parsing, which is inefficient for lengthy sentences and inaccurate for the sentences that do not follow proper grammatical structure, the proposed method applies n-gram techniques followed by various heuristics-based refinements to identify candidate phrases from text documents. A rich set of lexical and semantic features are identified to characterize the candidate phrases and determine their keyphraseness and relevance weights. The authors also propose a font-size determination function, which utilizes the relevance weights of the keyphrases to determine their relative font size for tag cloud visualization. The efficacy of the proposed framework is established through experimentation and its comparison with the existing state-of-the-art tag cloud generation methods.

The role of competence management in pharmacy is increasing as healthcare is a field where the fast development of drug treatment and the introduction of new drugs require specialized skills and... Show More

The role of competence management in pharmacy is increasing as healthcare is a field where the fast development of drug treatment and the introduction of new drugs require specialized skills and knowledge that must be renewed frequently. However, two obstacles for the widespread adoption of computer aided competence management in pharmacies exist. First, pharmacies do not have enough resources for maintaining competence management systems. Second, in pharmacies well-functioning competence management requires co-operation with other organizations, such as healthcare authorities and educational institutions, but competence management systems do not support such co-operation. To solve the resource problem, the authors have exploited a community cloud, which enables resource sharing among pharmacies and thus reduces pharmacies’ costs in competence management. To carry out co-operation with other organizations, they have developed a competence ontology which not only specify a shared terminology but also makes up a shared data store for the co-operating organizations. The competence ontology is specified in OWL. Further, to achieve efficiency in implementing the ontology the authors have transformed it into relational model. By means of relational database systems the authors have also achieved additional functionality, such as triggering, in competence management.