677 research outputs found

    Non-transferable unidirectional proxy re-encryption scheme for secure social cloud storage sharing

    Get PDF
    (c) 2016 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.Proxy re-encryption (PRE), introduced by Blaze et al. in 1998, allows a semi-trusted proxy with the re-encryption key to translatea ciphertext under the delegator into another ciphertext, which can be decrypted by the delegatee. In this process, the proxy is required to know nothing about the plaintext. Many PRE schemes have been proposed so far, however until now almost all the unidirectional PRE schemes suffer from the transferable property. That is, if the proxy and a set of delegatees collude, they can re-delegate the delegator's decryption rights to the other ones, while the delegator has no agreement on this. Thus designing non-transferable unidirectional PRE scheme is an important open research problem in the field. In this paper, we tackle this open problem by using the composite order bilinear pairing. Concretely, we design a non-transferable unidirectional PRE scheme based on Hohenberger et al.'s unidirectional PRE scheme. Furthermore, we discuss our scheme's application to secure cloud storage, especially for sharing private multimedia content for social cloud storage users.Peer ReviewedPostprint (author's final draft

    Apache Mahout’s k-Means vs. fuzzy k-Means performance evaluation

    Get PDF
    (c) 2016 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.The emergence of the Big Data as a disruptive technology for next generation of intelligent systems, has brought many issues of how to extract and make use of the knowledge obtained from the data within short times, limited budget and under high rates of data generation. The foremost challenge identified here is the data processing, and especially, mining and analysis for knowledge extraction. As the 'old' data mining frameworks were designed without Big Data requirements, a new generation of such frameworks is being developed fully implemented in Cloud platforms. One such frameworks is Apache Mahout aimed to leverage fast processing and analysis of Big Data. The performance of such new data mining frameworks is yet to be evaluated and potential limitations are to be revealed. In this paper we analyse the performance of Apache Mahout using large real data sets from the Twitter stream. We exemplify the analysis for the case of two clustering algorithms, namely, k-Means and Fuzzy k-Means, using a Hadoop cluster infrastructure for the experimental study.Peer ReviewedPostprint (author's final draft

    Identity based proxy re-encryption scheme (IBPRE+) for secure cloud data sharing

    Get PDF
    (c) 2016 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.In proxy re-encryption (PRE), a proxy with re-encryption keys can transfer aciphertext computed under Alice's public key into a new one, which can be decrypted by Bob only with his secret key. Recently, Wang et al. introduced the concept of PRE plus (PRE+) scheme, which can be seen as the dual of PRE, and is almost the same as PRE scheme except that the re-encryption keys are generated by the encrypter. Compared to PRE, PRE+ scheme can easily achieve two important properties: first, the message-level based fine-grained delegation and, second, the non-transferable property. In this paper, we extend the concept of PRE+ to the identity based setting. We propose a concrete IBPRE+ scheme based on 3-linear map and roughly discuss its properties. We also demonstrate potential application of this new primitive to secure cloud data sharing.Peer ReviewedPostprint (author's final draft

    A methodological approach to modelling trustworthiness in online collaborative learning

    Get PDF
    (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.Trustworthiness and technological security solutions are closely related to online collaborative learning as they can be combined with the aim of reaching information security requirements for e-Learning participants and designers. In this paper, we justify the need of trustworthiness models as a functional requirement devoted to improve information security. To this end, we propose a methodological approach to modelling trustworthiness in online collaborative learning. Our proposal sets out to build a theoretical approach with the aim to provide e-Learning designers and managers with guidelines for incorporating security into online collaborative activities through trustworthiness assessment and prediction.Peer ReviewedPostprint (author's final draft

    A fuzzy-based approach for classifying students' emotional states in online collaborative work

    Get PDF
    (c) 2016 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.Emotion awareness is becoming a key aspect in collaborative work at academia, enterprises and organizations that use collaborative group work in their activity. Due to pervasiveness of ICT's, most of collaboration can be performed through communication media channels such as discussion forums, social networks, etc. The emotive state of the users while they carry out their activity such as collaborative learning at Universities or project work at enterprises and organizations influences very much their performance and can actually determine the final learning or project outcome. Therefore, monitoring the users' emotive states and using that information for providing feedback and scaffolding is crucial. To this end, automated analysis over data collected from communication channels is a useful source. In this paper, we propose an approach to process such collected data in order to classify and assess emotional states of involved users and provide them feedback accordingly to their emotive states. In order to achieve this, a fuzzy approach is used to build the emotive classification system, which is fed with data from ANEW dictionary, whose words are bound to emotional weights and these, in turn, are used to map Fuzzy sets in our proposal. The proposed fuzzy-based system has been evaluated using real data from collaborative learning courses in an academic context.Peer ReviewedPostprint (author's final draft

    An information security model based on trustworthiness for enhancing security in on-line collaborative learning

    Get PDF
    L'objectiu principal d'aquesta tesi és incorporar propietats i serveis de la seguretat en sistemes d'informació en l'aprenentatge col·laboratiu en línia, seguint un model funcional basat en la valoració i predicció de la confiança. Aquesta tesi estableix com a punt de partença el disseny d'una solució de seguretat innovadora, basada en una metodologia pròpia per a oferir als dissenyadors i gestors de l'e-learning les línies mestres per a incorporar mesures de seguretat en l'aprenentatge col·laboratiu en línia. Aquestes guies cobreixen tots els aspectes sobre el disseny i la gestió que s'han de considerar en els processos relatius a l'e-learning, entre altres l'anàlisi de seguretat, el disseny d'activitats d'aprenentatge, la detecció d'accions anòmales o el processament de dades sobre confiança. La temàtica d'aquesta tesi té una naturalesa multidisciplinària i, al seu torn, les diferents disciplines que la formen estan íntimament relacionades. Les principals disciplines de què es tracta en aquesta tesi són l'aprenentatge col·laboratiu en línia, la seguretat en sistemes d'informació, els entorns virtuals d'aprenentatge (EVA) i la valoració i predicció de la confiança. Tenint en compte aquest àmbit d'aplicació, el problema de garantir la seguretat en els processos d'aprenentatge col·laboratiu en línia es resol amb un model híbrid construït sobre la base de solucions funcionals i tecnològiques, concretament modelatge de la confiança i solucions tecnològiques per a la seguretat en sistemes d'informació.El principal objetivo de esta tesis es incorporar propiedades y servicios de la seguridad en sistemas de información en el aprendizaje colaborativo en línea, siguiendo un modelo funcional basado en la valoración y predicción de la confianza. Esta tesis establece como punto de partida el diseño de una solución de seguridad innovadora, basada en una metodología propia para ofrecer a los diseñadores y gestores del e-learning las líneas maestras para incorporar medidas de seguridad en el aprendizaje colaborativo en línea. Estas guías cubren todos los aspectos sobre el diseño y la gestión que hay que considerar en los procesos relativos al e-learning, entre otros el análisis de la seguridad, el diseño de actividades de aprendizaje, la detección de acciones anómalas o el procesamiento de datos sobre confianza. La temática de esta tesis tiene una naturaleza multidisciplinar y, a su vez, las diferentes disciplinas que la forman están íntimamente relacionadas. Las principales disciplinas tratadas en esta tesis son el aprendizaje colaborativo en línea, la seguridad en sistemas de información, los entornos virtuales de aprendizaje (EVA) y la valoración y predicción de la confianza. Teniendo en cuenta este ámbito de aplicación, el problema de garantizar la seguridad en los procesos de aprendizaje colaborativo en línea se resuelve con un modelo híbrido construido en base a soluciones funcionales y tecnológicas, concretamente modelado de la confianza y soluciones tecnológicas para la seguridad en sistemas de información.This thesis' main goal is to incorporate information security properties and services into online collaborative learning using a functional approach based on trustworthiness assessment and prediction. As a result, this thesis aims to design an innovative security solution, based on methodological approaches, to provide e-learning designers and managers with guidelines for incorporating security into online collaborative learning. These guidelines include all processes involved in e-learning design and management, such as security analysis, learning activity design, detection of anomalous actions, trustworthiness data processing, and so on. The subject of this research is multidisciplinary in nature, with the different disciplines comprising it being closely related. The most significant ones are online collaborative learning, information security, learning management systems (LMS), and trustworthiness assessment and prediction models. Against this backdrop, the problem of securing collaborative online learning activities is tackled by a hybrid model based on functional and technological solutions, namely, trustworthiness modelling and information security technologies

    Security in online learning assessment towards an effective trustworthiness approach to support e-learning teams

    Get PDF
    (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.This paper proposes a trustworthiness model for the design of secure learning assessment in on-line collaborative learning groups. Although computer supported collaborative learning has been widely adopted in many educational institutions over the last decade, there exist still drawbacks which limit their potential in collaborative learning activities. Among these limitations, we investigate information security requirements in on-line assessment, (e-assessment), which can be developed in collaborative learning contexts. Despite information security enhancements have been developed in recent years, to the best of our knowledge, integrated and holistic security models have not been completely carried out yet. Even when security advanced methodologies and technologies are deployed in Learning Management Systems, too many types of vulnerabilities still remain opened and unsolved. Therefore, new models such as trustworthiness approaches can overcome these lacks and support e-assessment requirements for e-Learning. To this end, a trustworthiness model is designed in order to conduct the guidelines of a holistic security model for on-line collaborative learning through effective trustworthiness approaches. In addition, since users' trustworthiness analysis involves large amounts of ill-structured data, a parallel processing paradigm is proposed to build relevant information modeling trustworthiness levels for e-Learning.Peer ReviewedPostprint (author's final draft

    Software agents in large scale open e-learning: a critical component for the future of Massive Online Courses (MOOCs)

    Get PDF
    (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.MOOCs or massive open online courses are a recent trend in online education. They combine online resources with social tools and have unique challenges due to the large number of simultaneous participants. This paper analyzes some of the challenges in the areas of MOOC design, delivery and assessment. Then the authors present an approach using software agents to overcome some of the challenges that have been identified, as well as optimize efficiency, reduce costs, and ensure the pedagogical effectiveness and educational quality of large scale online learning courses. This paper is a first step towards research in the usage of software agents in massive online courses that we hope will shed more light on potential real life applications.Peer ReviewedPostprint (author's final draft

    Predicting trustworthiness behavior to enhance security in on-line assessment

    Get PDF
    (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.Over the last decade, information security has been considered a key issue in e-Learning design. Although security requirements can be met with advanced technological approaches and these solutions offer feasible methods in many e-Learning scenarios, on-line assessment activities usually show specific issues that cannot be solved with technology alone. In addition, security vulnerabilities in on-line assessment impede the development of an overall model devoted to manage secure on-line assessment. In this paper, we propose an innovative approach to enhance technological security solutions with trustworthiness. To this end, we endow previous trustworthiness models with prediction features by composing trustworthiness modeling and assessment, normalization methods, history sequences, and neural network-based approaches. In order to validate our approach, we present a peer-to-peer on-line assessment model carried out in a real online course.Peer ReviewedPostprint (author's final draft
    • …
    corecore