548 research outputs found

    A look at cloud architecture interoperability through standards

    Get PDF
    Enabling cloud infrastructures to evolve into a transparent platform while preserving integrity raises interoperability issues. How components are connected needs to be addressed. Interoperability requires standard data models and communication encoding technologies compatible with the existing Internet infrastructure. To reduce vendor lock-in situations, cloud computing must implement universal strategies regarding standards, interoperability and portability. Open standards are of critical importance and need to be embedded into interoperability solutions. Interoperability is determined at the data level as well as the service level. Corresponding modelling standards and integration solutions shall be analysed

    Privacy Preservation and Analytical Utility of E-Learning Data Mashups in the Web of Data

    Get PDF
    Virtual learning environments contain valuable data about students that can be correlated and analyzed to optimize learning. Modern learning environments based on data mashups that collect and integrate data from multiple sources are relevant for learning analytics systems because they provide insights into students' learning. However, data sets involved in mashups may contain personal information of sensitive nature that raises legitimate privacy concerns. Average privacy preservation methods are based on preemptive approaches that limit the published data in a mashup based on access control and authentication schemes. Such limitations may reduce the analytical utility of the data exposed to gain students' learning insights. In order to reconcile utility and privacy preservation of published data, this research proposes a new data mashup protocol capable of merging and k-anonymizing data sets in cloud-based learning environments without jeopardizing the analytical utility of the information. The implementation of the protocol is based on linked data so that data sets involved in the mashups are semantically described, thereby enabling their combination with relevant educational data sources. The k-anonymized data sets returned by the protocol still retain essential information for supporting general data exploration and statistical analysis tasks. The analytical and empirical evaluation shows that the proposed protocol prevents individuals' sensitive information from re-identifying.The Spanish National Research Agency (AEI) funded this research through the project CREPES (ref. PID2020-115844RB-I00) with ERDF funds

    Privacy Preserving Data-as-a-Service Mashups

    Get PDF
    Data-as-a-Service (DaaS) is a paradigm that provides data on demand to consumers across different cloud platforms over the Internet. Yet, a single DaaS provider may not be able to fulfill a data request. Consequently, the concept of DaaS mashup was introduced to enable DaaS providers to dynamically integrate their data on demand depending on consumers’ requests. Utilizing DaaS mashup, however, involves some challenges. Mashing up data from multiple sources to answer a consumer’s request might reveal sensitive information and thereby compromise the privacy of individuals. Moreover, data integration of arbitrary DaaS providers might not always be sufficient to answer incoming requests. In this thesis, we provide a cloud-based framework for privacy-preserving DaaS mashup that enables secure collaboration between DaaS providers for the purpose of generating an anonymous dataset to support data mining. We propose a greedy algorithm to determine a suitable group of DaaS providers whose data can satisfy a given request. Furthermore, our framework securely integrates the data from multiple DaaS providers while preserving the privacy of the resulting mashup data. Experiments on real-life data demonstrate that our DaaS mashup framework is scalable to large set of databases and it can efficiently and effectively satisfy the data privacy and data mining requirements specified by the DaaS providers and the data consumers

    10301 Executive Summary and Abstracts Collection -- Service Value Networks

    Get PDF
    From 25.07.2010 to 30.07.2010, the Perspectives Workshop 10301 ``Perspectives Workshop: Service Value Networks \u27\u27 was held in Schloss Dagstuhl~--~Leibniz Center for Informatics. During the seminar, several participants presented their current research, and ongoing work and open problems were discussed. Abstracts of the presentations given during the seminar as well as abstracts of seminar results and ideas are put together in this paper. The first section describes the seminar topics and goals in general. Links to extended abstracts or full papers are provided, if available

    Privacy-preserving distributed service recommendation based on locality-sensitive hashing

    Get PDF
    With the advent of IoT (Internet of Things) age, considerable web services are emerging rapidly in service communities, which places a heavy burden on the target users’ service selection decisions. In this situation, various techniques, e.g., collaborative filtering (i.e., CF) is introduced in service recommendation to alleviate the service selection burden. However, traditional CF-based service recommendation approaches often assume that the historical user-service quality data is centralized, while neglect the distributed recommendation situation. Generally, distributed service recommendation involves inevitable message communication among different parties and hence, brings challenging efficiency and privacy concerns. In view of this challenge, a novel privacy-preserving distributed service recommendation approach based on Locality-Sensitive Hashing (LSH), i.e., DistSRLSH is put forward in this paper. Through LSH, DistSRLSH can achieve a good tradeoff among service recommendation accuracy, privacy-preservation and efficiency in distributed environment. Finally, through a set of experiments deployed on WS-DREAM dataset, we validate the feasibility of our proposal in handling distributed service recommendation problems

    Service-Oriented Framework for Developing Interoperable e-Health Systems in a Low-Income Country

    Get PDF
    e-Health solutions in low-income countries are fragmented, address institution-specific needs, and do little to address the strategic need for inter-institutional exchange of health data. Although various e-health interoperability frameworks exist, contextual factors often hinder their effective adoption in low-income countries. This underlines the need to investigate such factors and to use findings to adapt existing e-health interoperability models. Following a design science approach, this research involved conducting an exploratory survey among 90 medical and Information Technology personnel from 67 health facilities in Uganda. Findings were used to derive requirements for e-health interoperability, and to orchestrate elements of a service oriented framework for developing interoperable e-health systems in a low-income country (SOFIEH). A service-oriented approach yields reusable, flexible, robust, and interoperable services that support communication through well-defined interfaces. SOFIEH was evaluated using structured walkthroughs, and findings indicate that it scored well regarding applicability, usability, and understandability

    Cognitive privacy middleware for deep learning mashup in environmental IoT

    Get PDF
    Data mashup is a Web technology that combines information from multiple sources into a single Web application. Mashup applications support new services, such as environmental monitoring. The different organizations utilize data mashup services to merge data sets from the different Internet of Multimedia Things (IoMT) context-based services in order to leverage the performance of their data analytics. However, mashup, different data sets from multiple sources, is a privacy hazard as it might reveal citizens specific behaviors in different regions. In this paper, we present our efforts to build a cognitive-based middleware for private data mashup (CMPM) to serve a centralized environmental monitoring service. The proposed middleware is equipped with concealment mechanisms to preserve the privacy of the merged data sets from multiple IoMT networks involved in the mashup application. In addition, we presented an IoT-enabled data mashup service, where the multimedia data are collected from the various IoMT platforms, and then fed into an environmental deep learning service in order to detect interesting patterns in hazardous areas. The viable features within each region were extracted using a multiresolution wavelet transform, and then fed into a discriminative classifier to extract various patterns. We also provide a scenario for IoMT-enabled data mashup service and experimentation results
    corecore