119,225 research outputs found

    An approach to building a secure and persistent distributed object management system

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    The Common Object Request Broker Architecture (CORBA) proposed by the Object Management Group (OMG) is a widely accepted standard to provide a system level framework in design and implementation of distributed objects. The core of the Object Management Architecture (OMA) is an Object Request Broker (ORB), which provides transparency of object location, activation, and communications. However, the specification provided by the OMG is not sufficient. For instance, there are no security specifications when handling object requests through the ORBs. The lack of such a security service prevents the use of CORBA from handling sensitive data such as personal and corporate financial information; In view of the above, this thesis identifies, explores, and provides an approach to handling secure objects in a distributed environment along with a persistent object service using the CORBA specification. The research specifically involves the design and implementation of a secured distributed object service. This object service requires a persistent service and object storage for storing and retrieving security specific information. To provide a secure distributed object environment, a secure object service using the specifications provided by the OMG has been designed and implemented. In addition, to preserve the persistence of secure information, an object service has been implemented to provide a persistent data store; The secure object service can provide a framework for handling distributed object in applications requiring security clearance such as distributed banking, online stock tradings, internet shopping, geographic and medical information systems

    A Machine Learning SDN-Enabled Big Data Model for IoMT System

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    [EN] In recent times, health applications have been gaining rapid popularity in smart cities using the Internet of Medical Things (IoMT). Many real-time solutions are giving benefits to both patients and professionals for remote data accessibility and suitable actions. However, timely medical decisions and efficient management of big data using IoT-based resources are the burning research challenges. Additionally, the distributed nature of data processing in many proposed solutions explicitly increases the threats of information leakages and damages the network integrity. Such solutions impose overhead on medical sensors and decrease the stability of the real-time transmission systems. Therefore, this paper presents a machine-learning model with SDN-enabled security to predict the consumption of network resources and improve the delivery of sensors data. Additionally, it offers centralized-based software define network (SDN) architecture to overcome the network threats among deployed sensors with nominal management cost. Firstly, it offers an unsupervised machine learning technique and decreases the communication overheads for IoT networks. Secondly, it predicts the link status using dynamic metrics and refines its strategies using SDN architecture. In the end, a security algorithm is utilized by the SDN controller that efficiently manages the consumption of the IoT nodes and protects it from unidentified occurrences. The proposed model is verified using simulations and improves system performance in terms of network throughput by 13%, data drop ratio by 39%, data delay by 11%, and faulty packets by 46% compared to HUNA and CMMA schemes.Haseeb, K.; Ahmad, I.; Iqbal Awan, I.; Lloret, J.; Bosch Roig, I. (2021). A Machine Learning SDN-Enabled Big Data Model for IoMT System. Electronics. 10(18):1-13. https://doi.org/10.3390/electronics10182228S113101

    Secure and Trustable Electronic Medical Records Sharing using Blockchain

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    Electronic medical records (EMRs) are critical, highly sensitive private information in healthcare, and need to be frequently shared among peers. Blockchain provides a shared, immutable and transparent history of all the transactions to build applications with trust, accountability and transparency. This provides a unique opportunity to develop a secure and trustable EMR data management and sharing system using blockchain. In this paper, we present our perspectives on blockchain based healthcare data management, in particular, for EMR data sharing between healthcare providers and for research studies. We propose a framework on managing and sharing EMR data for cancer patient care. In collaboration with Stony Brook University Hospital, we implemented our framework in a prototype that ensures privacy, security, availability, and fine-grained access control over EMR data. The proposed work can significantly reduce the turnaround time for EMR sharing, improve decision making for medical care, and reduce the overall costComment: AMIA 2017 Annual Symposium Proceeding
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