Cocoon: A System Architecture for Differentially Private Training with Correlated Noises

arXiv:2510.07304v2 Announce Type: replace-cross Abstract: 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 their real-world adoption. To improve accuracy, a new family of approaches adds carefully designed correlated noises, so that noises cancel out each other across iterations. We performed an extensive characterization study of these new mechanism

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