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• W2947741322 abstract "With the prosperity of machine learning and cloud computing, meaningful information can be mined from mass electronic medical data which help physicians make proper disease diagnosis for patients. However, using medical data and disease information of patients frequently raise privacy concerns. In this paper, based on single-layer perceptron, we propose a scheme of privacy-preserving clinical decision with cloud support (PPCD), which securely conducts disease model training and prediction for the patient. Each party learns nothing about the other's private information. In PPCD, a lightweight secure multiplication is presented and introduced to improve the model training. Security analysis and experimental results on real data confirm the high accuracy of disease prediction achieved by the proposed PPCD without the risk of privacy disclosure." @default.
• W2947741322 created "2019-06-07" @default.
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• W2947741322 date "2019-05-29" @default.
• W2947741322 modified "2023-10-14" @default.
• W2947741322 title "PPCD: Privacy-preserving clinical decision with cloud support" @default.
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• W2947741322 doi "https://doi.org/10.1371/journal.pone.0217349" @default.
• W2947741322 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6541381" @default.
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