1,721,127 research outputs found
MEXchange: A Privacy-preserving Blockchain-based Framework for Health Information Exchange using Ring Signature and Stealth Address
No full textHealth information exchange (HIE) refers to the integrated management and secure sharing of health information among healthcare entities. HIE improves healthcare quality and streamline healthcare administrative work. These advantages have propelled health-care stakeholders to implement HIE. However, challenged by issues such as security, privacy, and costs, HIE is not widespread. Recent studies have suggested blockchain-based HIE for solving security and privacy issues. Unfortunately, existing blockchain-based HIE studies do not consider the privacy issues caused by analyzing senders and receivers of transactions in the blockchain. In this work, we suggest MEXchange, a novel blockchain-based privacypreserving HIE that prevents the privacy issue by obscuring the sender and concealing receiver addresses. We propose smart contracts and workflow that use ring signature and stealth address for blockchainbased HIE. Software components and implementation of MEXchange on the Ethereum private network are discussed. We evaluate MEXchange quantitatively by measuring the transaction latency and throughput of exchanging. Also, we evaluate MEXchange qualitatively using the requirements of the Office of National Coordinator for Health Information Technology (ONC). Moreover, we proceed threat modeling based on STRIDE. Finally, we compare MEXchange with Ancile, FHIRChain, Integrating the Healthcare Enterprise Cross-Enterprise Document Sharing (IHE XDS), and MedRec. The MEXchange lowers barriers to the application of blockchain-based HIE systems by mitigating privacy and security issues among healthcare stakeholders.11Ysciescopu
Prediction-based resource allocation using LSTM and minimum cost and maximum flow algorithm
No full textPredictive business process monitoring aims at providing the predictions about running instances by analyzing logs of completed cases of a business process. Recently, a lot of research focuses on increasing productivity and efficiency in a business process by forecasting potential problems during its executions. However, most of the studies lack suggesting concrete actions to improve the process. They leave it up to the subjective judgment of a user. In this paper, we propose a novel method to connect the results from predictive business process monitoring to actual business process improvements. More in detail, we optimize the resource allocation in a non-clairvoyant online environment, where we have limited information required for scheduling, by exploiting the predictions. The proposed method integrates offline prediction model construction that predicts the processing time and the next activity of an ongoing instance using LSTM with online resource allocation that is extended from the minimum cost and maximum flow algorithm. To validate the proposed method, we performed experiments using an artificial event log and a real-life event log from a global financial organization.1
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