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.
Optimal federated learning for non- parametric regression with heterogeneous distributed differential privacy constraints.arXiv preprint arXiv:2406.06755
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
representative citing papers
Under clientwise sample-level zCDP, the Fisher information of any fully interactive public federated transcript contracts to a sum of per-client privacy-vs-sample terms, yielding matching minimax rates for mean, linear, and nonparametric regression.
Initiates finite-sample theory for differentially private hypothesis testing in survival analysis, with private tests for Cox models and cumulative hazards plus minimax bounds.
A random-projection differentially private kernel ERM method attains minimax-optimal excess risk bounds for squared and Lipschitz-smooth convex losses under local strong convexity, plus the first dimension-free bounds for objective-perturbation private linear ERM.
Introduces FedHybrid and FedNewton for DP federated M-estimation, with finite-sample MSE bounds, minimax lower bound, and evaluations on vision datasets.
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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A Van Trees Lower Bound for Fully Interactive Differentially Private Federated Learning
Under clientwise sample-level zCDP, the Fisher information of any fully interactive public federated transcript contracts to a sum of per-client privacy-vs-sample terms, yielding matching minimax rates for mean, linear, and nonparametric regression.
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Differentially private hypothesis testing in survival analysis
Initiates finite-sample theory for differentially private hypothesis testing in survival analysis, with private tests for Cox models and cumulative hazards plus minimax bounds.
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Optimal differentially private kernel learning with random projection
A random-projection differentially private kernel ERM method attains minimax-optimal excess risk bounds for squared and Lipschitz-smooth convex losses under local strong convexity, plus the first dimension-free bounds for objective-perturbation private linear ERM.
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Statistical Limits and Efficient Algorithms for Differentially Private Federated Learning
Introduces FedHybrid and FedNewton for DP federated M-estimation, with finite-sample MSE bounds, minimax lower bound, and evaluations on vision datasets.
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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.