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- W4309676776 abstract "This letter proposes a novel secure data protection model for privacy preservation in the cloud environment by partitioning, sanitizing, and analyzing the data effectively to improve the model’s privacy. Several experiments and comparisons of the proposed model with existing works indicate that it protects data with high accuracy, precision, recall, and F1-score up to 87.03%, 84.87%, 87.03%, and 85.00%, for diverse datasets with a relative improvement up to 15.89%, 27.73%, 15.89%, and 21.44%, respectively." @default.
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- W4309676776 date "2022-12-01" @default.
- W4309676776 modified "2023-10-14" @default.
- W4309676776 title "Differential and TriPhase Adaptive Learning-Based Privacy-Preserving Model for Medical Data in Cloud Environment" @default.
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- W4309676776 doi "https://doi.org/10.1109/lnet.2022.3215248" @default.
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