NetPTR achieves edge-DP spectral clustering for ordinary networks and column-node-DP for bipartite networks, with consistency guarantees separating non-private error from privacy error under degree-corrected block models in sparse regimes.
arXiv preprint arXiv:2002.08774 , year=
3 Pith papers cite this work. Polarity classification is still indexing.
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2026 3verdicts
UNVERDICTED 3representative citing papers
Defines threshold breakdown point and m-sensitivity for M-estimators, derives their properties, extends to hypothesis testing, and supplies consistency, asymptotic normality, and multiplier bootstrap results.
Introduces ePTR pipeline using safety lower bound testing to enable optimal DP mechanisms for sensitive estimators in classification and regression.
citing papers explorer
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NetPTR: Optimal Differentially Private Spectral Community Detection on Sparse Networks
NetPTR achieves edge-DP spectral clustering for ordinary networks and column-node-DP for bipartite networks, with consistency guarantees separating non-private error from privacy error under degree-corrected block models in sparse regimes.
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The Threshold Breakdown Point
Defines threshold breakdown point and m-sensitivity for M-estimators, derives their properties, extends to hypothesis testing, and supplies consistency, asymptotic normality, and multiplier bootstrap results.
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Efficient Propose-Test-Release for Optimal Differentially Private Estimation
Introduces ePTR pipeline using safety lower bound testing to enable optimal DP mechanisms for sensitive estimators in classification and regression.