Cover Image

On the Statistical Complexity of Estimation and Testing under Privacy Constraints

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Lalanne, Clément, Garivier, Aurélien, Gribonval, Rémi
Format:	Preprint
Published:	2022
Subjects:	Machine Learning Cryptography and Security
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

About the Cost of Central Privacy in Density Estimation
by: Lalanne, Clément, et al.
Published: (2023)

Rényi Pufferfish Privacy: General Additive Noise Mechanisms and Privacy Amplification by Iteration
by: Pierquin, Clément, et al.
Published: (2023)

Privacy Amplification Through Synthetic Data: Insights from Linear Regression
by: Pierquin, Clément, et al.
Published: (2025)

Privacy Amplification Persists under Unlimited Synthetic Data Release
by: Pierquin, Clément, et al.
Published: (2026)

Privacy Attacks in Decentralized Learning
by: Mrini, Abdellah El, et al.
Published: (2024)

Can sparsity improve the privacy of neural networks?
by: Gonon, Antoine, et al.
Published: (2023)

Tighter Privacy Auditing of DP-SGD in the Hidden State Threat Model
by: Cebere, Tudor, et al.
Published: (2024)

Token-Efficient Change Detection in LLM APIs
by: Chauvin, Timothée, et al.
Published: (2026)

Improved Communication-Privacy Trade-offs in $L_2$ Mean Estimation under Streaming Differential Privacy
by: Chen, Wei-Ning, et al.
Published: (2024)

Adaptive Methods Are Preferable in High Privacy Settings: An SDE Perspective
by: Compagnoni, Enea Monzio, et al.
Published: (2026)

A Huber Loss Minimization Approach to Mean Estimation under User-level Differential Privacy
by: Zhao, Puning, et al.
Published: (2024)

Privacy-Preserving Statistical Data Generation: Application to Sepsis Detection
by: Macias-Fassio, Eric, et al.
Published: (2024)

A Statistical Viewpoint on Differential Privacy: Hypothesis Testing, Representation and Blackwell's Theorem
by: Su, Weijie J.
Published: (2024)

The Sample Complexity of Membership Inference and Privacy Auditing
by: Haghifam, Mahdi, et al.
Published: (2025)

Sparsity in neural networks can improve their privacy
by: Gonon, Antoine, et al.
Published: (2023)

Optimal Fairness under Local Differential Privacy
by: Ghoukasian, Hrad, et al.
Published: (2025)

WaKA: Data Attribution using K-Nearest Neighbors and Membership Privacy Principles
by: Mesana, Patrick, et al.
Published: (2024)

One-shot Empirical Privacy Estimation for Federated Learning
by: Andrew, Galen, et al.
Published: (2023)

Auditing Differential Privacy Guarantees Using Density Estimation
by: Koskela, Antti, et al.
Published: (2024)

Private Estimation when Data and Privacy Demands are Correlated
by: Chaudhuri, Syomantak, et al.
Published: (2024)

Complex-valued Federated Learning with Differential Privacy and MRI Applications
by: Riess, Anneliese, et al.
Published: (2021)

Deep Learning under Fractional-Order Differential Privacy
by: Partohaghighi, Mohammad, et al.
Published: (2026)

Robustness Implies Privacy in Statistical Estimation
by: Hopkins, Samuel B., et al.
Published: (2022)

Insufficient Statistics Perturbation: Stable Estimators for Private Least Squares
by: Brown, Gavin, et al.
Published: (2024)

Privacy-Preserving Race/Ethnicity Estimation for Algorithmic Bias Measurement in the U.S
by: Badrinarayanan, Saikrishna, et al.
Published: (2024)

P3LS: Partial Least Squares under Privacy Preservation
by: Duy, Du Nguyen, et al.
Published: (2024)

Sampling-Free Privacy Accounting for Matrix Mechanisms under Random Allocation
by: Schuchardt, Jan, et al.
Published: (2026)

On Evaluating the Poisoning Robustness of Federated Learning under Local Differential Privacy
by: Wang, Zijian, et al.
Published: (2025)

Beyond Statistical Estimation: Differentially Private Individual Computation via Shuffling
by: Wang, Shaowei, et al.
Published: (2024)

A Differentially Private Kaplan-Meier Estimator for Privacy-Preserving Survival Analysis
by: Veeraragavan, Narasimha Raghavan, et al.
Published: (2024)

Degree-Preserving Randomized Response for Graph Neural Networks under Local Differential Privacy
by: Hidano, Seira, et al.
Published: (2022)

Minimax Optimal Two-Sample Testing under Local Differential Privacy
by: Mun, Jongmin, et al.
Published: (2024)

FedP3: Federated Personalized and Privacy-friendly Network Pruning under Model Heterogeneity
by: Yi, Kai, et al.
Published: (2024)

Jacobian-Guided Anisotropic Noise Reshaping for Enhancing Representation Utility under Local Differential Privacy
by: Ha, Youngmok, et al.
Published: (2026)

Differentially Private Decentralized Learning with Random Walks
by: Cyffers, Edwige, et al.
Published: (2024)

The Discrete Gaussian for Differential Privacy
by: Canonne, Clément L., et al.
Published: (2020)

A Bias-Accuracy-Privacy Trilemma for Statistical Estimation
by: Kamath, Gautam, et al.
Published: (2023)

Revisiting ML Training under Fully Homomorphic Encryption: Convergence Guarantees, Differential Privacy, and Efficient Algorithms
by: Zhou, Yvonne, et al.
Published: (2026)

Private Linear Regression with Differential Privacy and PAC Privacy
by: Yang, Hillary, et al.
Published: (2024)

Privacy Drift: Evolving Privacy Concerns in Incremental Learning
by: Ahamed, Sayyed Farid, et al.
Published: (2024)