A matrix shuffling mechanism for edge-differentially private spectral clustering achieves Õ(1/n) misclassification error via privacy amplification and a unified Davis-Kahan plus margin analysis, outperforming Analyze Gauss and noisy power iteration.
Stronger privacy amplification by shuffling for R´ enyi and approximate differential privacy.arXiv preprint arXiv:2208.04591, 2023
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Differentially Private Spectral Graph Clustering: Balancing Privacy, Accuracy, and Efficiency
A matrix shuffling mechanism for edge-differentially private spectral clustering achieves Õ(1/n) misclassification error via privacy amplification and a unified Davis-Kahan plus margin analysis, outperforming Analyze Gauss and noisy power iteration.