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Cocoon: A System Architecture for Differentially Private Training with Correlated Noises
Machine learning (ML) models memorize and leak training data, causing serious privacy issues to data owners. Training algorithms with differential privacy (DP) have been gaining attention as a solution. However, these algorithms add noise at each training iteration and degrade accuracy, limiting the
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