Image de couverture de livre

Correlated Noise Mechanisms for Differentially Private Learning

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Pillutla, Krishna, Upadhyay, Jalaj, Choquette-Choo, Christopher A., Dvijotham, Krishnamurthy, Ganesh, Arun, Henzinger, Monika, Katz, Jonathan, McKenna, Ryan, McMahan, H. Brendan, Rush, Keith, Steinke, Thomas, Thakurta, Abhradeep
Format:	Preprint
Publié:	2025
Sujets:	Machine Learning Cryptography and Security
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Efficient and Near-Optimal Noise Generation for Streaming Differential Privacy
par: Dvijotham, Krishnamurthy, et autres
Publié: (2024)

Correlated Noise Provably Beats Independent Noise for Differentially Private Learning
par: Choquette-Choo, Christopher A., et autres
Publié: (2023)

On Design Principles for Private Adaptive Optimizers
par: Ganesh, Arun, et autres
Publié: (2025)

Hush! Protecting Secrets During Model Training: An Indistinguishability Approach
par: Ganesh, Arun, et autres
Publié: (2025)

Fine-Tuning Large Language Models with User-Level Differential Privacy
par: Charles, Zachary, et autres
Publié: (2024)

Privacy Amplification for Matrix Mechanisms
par: Choquette-Choo, Christopher A., et autres
Publié: (2023)

An Inversion Theorem for Buffered Linear Toeplitz (BLT) Matrices and Applications to Streaming Differential Privacy
par: McMahan, H. Brendan, et autres
Publié: (2025)

Optimal Rates for $O(1)$-Smooth DP-SCO with a Single Epoch and Large Batches
par: Choquette-Choo, Christopher A., et autres
Publié: (2024)

It's My Data Too: Private ML for Datasets with Multi-User Training Examples
par: Ganesh, Arun, et autres
Publié: (2025)

Near Exact Privacy Amplification for Matrix Mechanisms
par: Choquette-Choo, Christopher A., et autres
Publié: (2024)

Improved Differentially Private Continual Observation Using Group Algebra
par: Henzinger, Monika, et autres
Publié: (2024)

Gradient Descent with Linearly Correlated Noise: Theory and Applications to Differential Privacy
par: Koloskova, Anastasia, et autres
Publié: (2023)

Almost Tight Error Bounds on Differentially Private Continual Counting
par: Henzinger, Monika, et autres
Publié: (2022)

InvisibleInk: High-Utility and Low-Cost Text Generation with Differential Privacy
par: Vinod, Vishnu, et autres
Publié: (2025)

Near-Optimal Generalized Private Testing
par: Chaturvedi, Anamay, et autres
Publié: (2026)

Constant matters: Fine-grained Complexity of Differentially Private Continual Observation
par: Fichtenberger, Hendrik, et autres
Publié: (2022)

Binned Group Algebra Factorization for Differentially Private Continual Counting
par: Henzinger, Monika, et autres
Publié: (2025)

Normalized Square Root: Sharper Matrix Factorization Bounds for Differentially Private Continual Counting
par: Henzinger, Monika, et autres
Publié: (2025)

Private Gradient Descent for Linear Regression: Tighter Error Bounds and Instance-Specific Uncertainty Estimation
par: Brown, Gavin, et autres
Publié: (2024)

The Last Iterate Advantage: Empirical Auditing and Principled Heuristic Analysis of Differentially Private SGD
par: Steinke, Thomas, et autres
Publié: (2024)

Back to Square Roots: An Optimal Bound on the Matrix Factorization Error for Multi-Epoch Differentially Private SGD
par: Kalinin, Nikita P., et autres
Publié: (2025)

JAX-Privacy: A library for differentially private machine learning
par: McKenna, Ryan, et autres
Publié: (2026)

A Hassle-free Algorithm for Private Learning in Practice: Don't Use Tree Aggregation, Use BLTs
par: McMahan, H. Brendan, et autres
Publié: (2024)

Deterministic Policies for Constrained Reinforcement Learning in Polynomial Time
par: McMahan, Jeremy
Publié: (2024)

Polynomial-Time Approximability of Constrained Reinforcement Learning
par: McMahan, Jeremy
Publié: (2025)

Anytime-Constrained Equilibria in Polynomial Time
par: McMahan, Jeremy
Publié: (2024)

Differentially Private Parameter-Efficient Fine-tuning for Large ASR Models
par: Liu, Hongbin, et autres
Publié: (2024)

Scaling up the Banded Matrix Factorization Mechanism for Differentially Private ML
par: McKenna, Ryan
Publié: (2024)

Recycling Scraps: Improving Private Learning by Leveraging Intermediate Checkpoints
par: Shejwalkar, Virat, et autres
Publié: (2022)

Objectively measuring subjectively described traits: geographic variation in body shape and caudal coloration pattern within Vieja melanura (Teleostei: Cichlidae)
par: Caleb D. McMahan
Publié: (2017)

Anytime-Constrained Reinforcement Learning
par: McMahan, Jeremy, et autres
Publié: (2023)

User Inference Attacks on Large Language Models
par: Kandpal, Nikhil, et autres
Publié: (2023)

Continual Counting with Gradual Privacy Expiration
par: Andersson, Joel Daniel, et autres
Publié: (2024)

Differentially Private Stream Processing at Scale
par: Zhang, Bing, et autres
Publié: (2023)

Differentially Private Decentralized Learning with Random Walks
par: Cyffers, Edwige, et autres
Publié: (2024)

Reinforced Generation of Combinatorial Structures: Ramsey Numbers
par: Nagda, Ansh, et autres
Publié: (2026)

Reinforced Generation of Combinatorial Structures: Hardness of Approximation
par: Nagda, Ansh, et autres
Publié: (2025)

Training Large ASR Encoders with Differential Privacy
par: Chauhan, Geeticka, et autres
Publié: (2024)

Making Sense of Sensemaking: Designing Authentic K-12 STEM Learning Experiences
par: T. J. McKenna
Publié: (2025)

Libraries and the Internet. ERIC Digest.
par: McKenna, Mary
Publié: (1994)