Cover Image

DMM: Distributed Matrix Mechanism for Differentially-Private Federated Learning Based on Constant-Overhead Linear Secret Resharing

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Bienstock, Alexander, Kumar, Ujjwal, Polychroniadou, Antigoni
Format:	Preprint
Published:	2024
Subjects:	Cryptography and Security Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

TAPAS: Efficient Two-Server Asymmetric Private Aggregation Beyond Prio(+)
by: Karthikeyan, Harish, et al.
Published: (2026)

Flamingo: Multi-Round Single-Server Secure Aggregation with Applications to Private Federated Learning
by: Ma, Yiping, et al.
Published: (2023)

Bounding the Excess Risk for Linear Models Trained on Marginal-Preserving, Differentially-Private, Synthetic Data
by: Zhou, Yvonne, et al.
Published: (2024)

$\mathsf{OPA}$: One-shot Private Aggregation with Single Client Interaction and its Applications to Federated Learning
by: Karthikeyan, Harish, et al.
Published: (2024)

ZKBoost: Zero-Knowledge Verifiable Training for XGBoost
by: Melissaris, Nikolas, et al.
Published: (2026)

Scalable Secure Biometric Authentication without Auxiliary Identifiers
by: Bienstock, Alexander, et al.
Published: (2026)

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

Armadillo: Robust Single-Server Secure Aggregation for Federated Learning with Input Validation
by: Ma, Yiping, et al.
Published: (2025)

Atlas-X Equity Financing: Unlocking New Methods to Securely Obfuscate Axe Inventory Data Based on Differential Privacy
by: Polychroniadou, Antigoni, et al.
Published: (2024)

Private and Communication-Efficient Federated Learning based on Differentially Private Sketches
by: Zhang, Meifan, et al.
Published: (2024)

Bayesian Pseudo Posterior Mechanism for Differentially Private Machine Learning
by: Chew, Robert, et al.
Published: (2025)

Correlated Noise Mechanisms for Differentially Private Learning
by: Pillutla, Krishna, et al.
Published: (2025)

Differentially Private Federated Learning: A Systematic Review
by: Fu, Jie, et al.
Published: (2024)

Differentially Private Linear Bandits with Partial Distributed Feedback
by: Li, Fengjiao, et al.
Published: (2022)

Differentially Private Federated Learning without Noise Addition: When is it Possible?
by: Zhang, Jiang, et al.
Published: (2024)

ALI-DPFL: Differentially Private Federated Learning with Adaptive Local Iterations
by: Ling, Xinpeng, et al.
Published: (2023)

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

Mitigating Noise Detriment in Differentially Private Federated Learning with Model Pre-training
by: Jin, Huitong, et al.
Published: (2024)

On Using Secure Aggregation in Differentially Private Federated Learning with Multiple Local Steps
by: Heikkilä, Mikko A.
Published: (2024)

Banded Square Root Matrix Factorization for Differentially Private Model Training
by: Kalinin, Nikita P., et al.
Published: (2024)

A Differentially Private Blockchain-Based Approach for Vertical Federated Learning
by: Tran, Linh, et al.
Published: (2024)

Differentially Private Sparse Linear Regression with Heavy-tailed Responses
by: Tian, Xizhi, et al.
Published: (2025)

Differentially Private Clustered Federated Learning with Privacy-Preserving Initialization and Normality-Driven Aggregation
by: Xu, Jie, et al.
Published: (2026)

Differentially Private Low-Rank Adaptation of Large Language Model Using Federated Learning
by: Liu, Xiao-Yang, et al.
Published: (2023)

Indifferential Privacy: A New Paradigm and Its Applications to Optimal Matching in Dark Pool Auctions
by: Polychroniadou, Antigoni, et al.
Published: (2025)

Correlated Privacy Mechanisms for Differentially Private Distributed Mean Estimation
by: Vithana, Sajani, et al.
Published: (2024)

S-BDT: Distributed Differentially Private Boosted Decision Trees
by: Peinemann, Thorsten, et al.
Published: (2023)

Differentially Private Random Feature Model
by: Liao, Chunyang, et al.
Published: (2024)

CorBin-FL: A Differentially Private Federated Learning Mechanism using Common Randomness
by: Salehi, Hojat Allah, et al.
Published: (2024)

Privately Learning Decision Lists and a Differentially Private Winnow
by: Bun, Mark, et al.
Published: (2026)

LanFL: Differentially Private Federated Learning with Large Language Models using Synthetic Samples
by: Wu, Huiyu, et al.
Published: (2024)

SPriFed-OMP: A Differentially Private Federated Learning Algorithm for Sparse Basis Recovery
by: Mulay, Ajinkya Kiran, et al.
Published: (2024)

Enabling Differentially Private Federated Learning for Speech Recognition: Benchmarks, Adaptive Optimizers and Gradient Clipping
by: Pelikan, Martin, et al.
Published: (2023)

Improving the Privacy and Practicality of Objective Perturbation for Differentially Private Linear Learners
by: Redberg, Rachel, et al.
Published: (2023)

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

DPIS: An Enhanced Mechanism for Differentially Private SGD with Importance Sampling
by: Wei, Jianxin, et al.
Published: (2022)

Differentially Private Federated $k$-Means Clustering with Server-Side Data
by: Scott, Jonathan, et al.
Published: (2025)

DictPFL: Efficient and Private Federated Learning on Encrypted Gradients
by: Xue, Jiaqi, et al.
Published: (2025)

Distributed Differentially Private Data Analytics via Secure Sketching
by: Burkhardt, Jakob, et al.
Published: (2024)

Differentially Private Sampling from Distributions via Wasserstein Projection
by: Takakura, Shokichi, et al.
Published: (2026)