A Private Approximation of the 2nd-Moment Matrix of Any Subsamplable Input
Fuente:
arXiv
Saved in:
| Main Authors: | Mahpud, Bar, Sheffet, Or |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Differentially Private Learning of Exponential Distributions: Simple Algorithms and Tight Bounds
by: Mahpud, Bar, et al.
Published: (2025)
by: Mahpud, Bar, et al.
Published: (2025)
Private Approximations of a Convex Hull in Low Dimensions
by: Gao, Yue, et al.
Published: (2020)
by: Gao, Yue, et al.
Published: (2020)
Optimal Bounds for Private Minimum Spanning Trees via Input Perturbation
by: Pagh, Rasmus, et al.
Published: (2024)
by: Pagh, Rasmus, et al.
Published: (2024)
Scalable DP-SGD: Shuffling vs. Poisson Subsampling
by: Chua, Lynn, et al.
Published: (2024)
by: Chua, Lynn, et al.
Published: (2024)
Avoiding Pitfalls for Privacy Accounting of Subsampled Mechanisms under Composition
by: Lebeda, Christian Janos, et al.
Published: (2024)
by: Lebeda, Christian Janos, et al.
Published: (2024)
Scaling up the Banded Matrix Factorization Mechanism for Differentially Private ML
by: McKenna, Ryan
Published: (2024)
by: McKenna, Ryan
Published: (2024)
Nearly-Linear Time Private Hypothesis Selection with the Optimal Approximation Factor
by: Aliakbarpour, Maryam, et al.
Published: (2025)
by: Aliakbarpour, Maryam, et al.
Published: (2025)
Continual Release of Densest Subgraphs: Privacy Amplification & Sublinear Space via Subsampling
by: Zhou, Felix
Published: (2025)
by: Zhou, Felix
Published: (2025)
Normalized Square Root: Sharper Matrix Factorization Bounds for Differentially Private Continual Counting
by: Henzinger, Monika, et al.
Published: (2025)
by: Henzinger, Monika, et al.
Published: (2025)
Improved Accuracy for Private Continual Cardinality Estimation in Fully Dynamic Streams via Matrix Factorization
by: Andersson, Joel Daniel, et al.
Published: (2026)
by: Andersson, Joel Daniel, et al.
Published: (2026)
Private Selection with Heterogeneous Sensitivities
by: Antonova, Daniela, et al.
Published: (2025)
by: Antonova, Daniela, et al.
Published: (2025)
Private Learning of Littlestone Classes, Revisited
by: Lyu, Xin
Published: (2025)
by: Lyu, Xin
Published: (2025)
Private Statistical Estimation via Truncation
by: Zampetakis, Manolis, et al.
Published: (2025)
by: Zampetakis, Manolis, et al.
Published: (2025)
Private Continual Counting of Unbounded Streams
by: Jacobsen, Ben, et al.
Published: (2025)
by: Jacobsen, Ben, et al.
Published: (2025)
Efficiently Computing Similarities to Private Datasets
by: Backurs, Arturs, et al.
Published: (2024)
by: Backurs, Arturs, et al.
Published: (2024)
How Private are DP-SGD Implementations?
by: Chua, Lynn, et al.
Published: (2024)
by: Chua, Lynn, et al.
Published: (2024)
Differentially Private Clustering in Data Streams
by: Epasto, Alessandro, et al.
Published: (2023)
by: Epasto, Alessandro, et al.
Published: (2023)
Faster Private Minimum Spanning Trees
by: Pagh, Rasmus, et al.
Published: (2024)
by: Pagh, Rasmus, et al.
Published: (2024)
A Smooth Binary Mechanism for Efficient Private Continual Observation
by: Andersson, Joel Daniel, et al.
Published: (2023)
by: Andersson, Joel Daniel, et al.
Published: (2023)
A Differentially Private Clustering Algorithm for Well-Clustered Graphs
by: He, Weiqiang, et al.
Published: (2024)
by: He, Weiqiang, et al.
Published: (2024)
Private Geometric Median in Nearly-Linear Time
by: Kumar, Syamantak, et al.
Published: (2025)
by: Kumar, Syamantak, et al.
Published: (2025)
Streaming Private Continual Counting via Binning
by: Andersson, Joel Daniel, et al.
Published: (2024)
by: Andersson, Joel Daniel, et al.
Published: (2024)
PLAN: Variance-Aware Private Mean Estimation
by: Aumüller, Martin, et al.
Published: (2023)
by: Aumüller, Martin, et al.
Published: (2023)
Differentially Private Multi-Sampling from Distributions
by: Cheu, Albert, et al.
Published: (2024)
by: Cheu, Albert, et al.
Published: (2024)
Not All Learnable Distribution Classes are Privately Learnable
by: Bun, Mark, et al.
Published: (2024)
by: Bun, Mark, et al.
Published: (2024)
Differentially Private Learning Beyond the Classical Dimensionality Regime
by: Dwork, Cynthia, et al.
Published: (2024)
by: Dwork, Cynthia, et al.
Published: (2024)
Privacy-Computation trade-offs in Private Repetition and Metaselection
by: Talwar, Kunal
Published: (2024)
by: Talwar, Kunal
Published: (2024)
Making Old Things New: A Unified Algorithm for Differentially Private Clustering
by: la Tour, Max Dupré, et al.
Published: (2024)
by: la Tour, Max Dupré, et al.
Published: (2024)
A Polynomial Time, Pure Differentially Private Estimator for Binary Product Distributions
by: Singhal, Vikrant
Published: (2023)
by: Singhal, Vikrant
Published: (2023)
Better Private Distribution Testing by Leveraging Unverified Auxiliary Data
by: Aliakbarpour, Maryam, et al.
Published: (2025)
by: Aliakbarpour, Maryam, et al.
Published: (2025)
PREAMBLE: Private and Efficient Aggregation via Block Sparse Vectors
by: Asi, Hilal, et al.
Published: (2025)
by: Asi, Hilal, et al.
Published: (2025)
Almost Tight Error Bounds on Differentially Private Continual Counting
by: Henzinger, Monika, et al.
Published: (2022)
by: Henzinger, Monika, et al.
Published: (2022)
Individual Privacy Accounting for Differentially Private Stochastic Gradient Descent
by: Yu, Da, et al.
Published: (2022)
by: Yu, Da, et al.
Published: (2022)
On Differentially Private Subspace Estimation in a Distribution-Free Setting
by: Tsfadia, Eliad
Published: (2024)
by: Tsfadia, Eliad
Published: (2024)
Differentially Private Bootstrap: New Privacy Analysis and Inference Strategies
by: Wang, Zhanyu, et al.
Published: (2022)
by: Wang, Zhanyu, et al.
Published: (2022)
Private PAC Learning May be Harder than Online Learning
by: Bun, Mark, et al.
Published: (2024)
by: Bun, Mark, et al.
Published: (2024)
Adaptive Batch Size for Privately Finding Second-Order Stationary Points
by: Liu, Daogao, et al.
Published: (2024)
by: Liu, Daogao, et al.
Published: (2024)
Differential Private Stochastic Optimization with Heavy-tailed Data: Towards Optimal Rates
by: Zhao, Puning, et al.
Published: (2024)
by: Zhao, Puning, et al.
Published: (2024)
DPSW-Sketch: A Differentially Private Sketch Framework for Frequency Estimation over Sliding Windows (Technical Report)
by: Wang, Yiping, et al.
Published: (2024)
by: Wang, Yiping, et al.
Published: (2024)
Private Stochastic Convex Optimization with Heavy Tails: Near-Optimality from Simple Reductions
by: Asi, Hilal, et al.
Published: (2024)
by: Asi, Hilal, et al.
Published: (2024)
Similar Items
-
Differentially Private Learning of Exponential Distributions: Simple Algorithms and Tight Bounds
by: Mahpud, Bar, et al.
Published: (2025) -
Private Approximations of a Convex Hull in Low Dimensions
by: Gao, Yue, et al.
Published: (2020) -
Optimal Bounds for Private Minimum Spanning Trees via Input Perturbation
by: Pagh, Rasmus, et al.
Published: (2024) -
Scalable DP-SGD: Shuffling vs. Poisson Subsampling
by: Chua, Lynn, et al.
Published: (2024) -
Avoiding Pitfalls for Privacy Accounting of Subsampled Mechanisms under Composition
by: Lebeda, Christian Janos, et al.
Published: (2024)