Cover Image

Privacy Amplification by Missing Data

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Roburin, Simon, Pinot, Rafaël, Scornet, Erwan
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Increasing Missingness to Reduce Bias: Richardson-SGD with Missing Data
by: Genans, Ferdinand, et al.
Published: (2026)

When Pattern-by-Pattern Works: Theoretical and Empirical Insights for Logistic Models with Missing Values
by: Muller, Christophe, et al.
Published: (2025)

Fairness Meets Privacy: Integrating Differential Privacy and Demographic Parity in Multi-class Classification
by: Say, Lilian, et al.
Published: (2025)

Principled Federated Random Forests for Heterogeneous Data
by: Khellaf, Rémi, et al.
Published: (2026)

Do we need rebalancing strategies? A theoretical and empirical study around SMOTE and its variants
by: Sakho, Abdoulaye, et al.
Published: (2024)

Harnessing Mixed Features for Imbalance Data Oversampling: Application to Bank Customers Scoring
by: Sakho, Abdoulaye, et al.
Published: (2025)

Asymptotic Normality of Infinite Centered Random Forests -Application to Imbalanced Classification
by: Mayala, Moria, et al.
Published: (2025)

On the Relevance of Byzantine Robust Optimization Against Data Poisoning
by: Farhadkhani, Sadegh, et al.
Published: (2024)

Random features models: a way to study the success of naive imputation
by: Ayme, Alexis, et al.
Published: (2024)

Equalized Generative Treatment: Matching f-divergences for Fairness in Generative Models
by: Verine, Alexandre, et al.
Published: (2026)

On the consistency of supervised learning with missing values
by: Josse, Julie, et al.
Published: (2019)

Leveraging Randomness in Model and Data Partitioning for Privacy Amplification
by: Dong, Andy, et al.
Published: (2025)

Privacy Amplification for Matrix Mechanisms
by: Choquette-Choo, Christopher A., et al.
Published: (2023)

Unified Mechanism-Specific Amplification by Subsampling and Group Privacy Amplification
by: Schuchardt, Jan, et al.
Published: (2024)

Overcoming the Challenges of Batch Normalization in Federated Learning
by: Guerraoui, Rachid, et al.
Published: (2024)

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)

Personalized Privacy Amplification via Importance Sampling
by: Fay, Dominik, et al.
Published: (2023)

ByzFL: Research Framework for Robust Federated Learning
by: González, Marc, et al.
Published: (2025)

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

Fine-Tuning Personalization in Federated Learning to Mitigate Adversarial Clients
by: Allouah, Youssef, et al.
Published: (2024)

Privacy Amplification in Differentially Private Zeroth-Order Optimization with Hidden States
by: Chien, Eli, et al.
Published: (2025)

Privacy Amplification for the Gaussian Mechanism via Bounded Support
by: Hu, Shengyuan, et al.
Published: (2024)

Unveiling the Role of Randomization in Multiclass Adversarial Classification: Insights from Graph Theory
by: Gnecco-Heredia, Lucas, et al.
Published: (2025)

Byzantine-Robust Federated Learning: Impact of Client Subsampling and Local Updates
by: Allouah, Youssef, et al.
Published: (2024)

Latent Diffusion for Missing Data
by: Estad, Alberte Heering, et al.
Published: (2026)

Privacy Amplification by Structured Subsampling for Deep Differentially Private Time Series Forecasting
by: Schuchardt, Jan, et al.
Published: (2025)

It's Our Loss: No Privacy Amplification for Hidden State DP-SGD With Non-Convex Loss
by: Annamalai, Meenatchi Sundaram Muthu Selva
Published: (2024)

Adaptive Optimization for Prediction with Missing Data
by: Bertsimas, Dimitris, et al.
Published: (2024)

Score Matching With Missing Data
by: Givens, Josh, et al.
Published: (2025)

Robust Federated Inference
by: Dhasade, Akash, et al.
Published: (2025)

Muffliato: Peer-to-Peer Privacy Amplification for Decentralized Optimization and Averaging
by: Cyffers, Edwige, et al.
Published: (2022)

Order-Agnostic Autoregressive Modelling with Missing Data
by: Peis, Ignacio, et al.
Published: (2026)

Extending Model-x Framework to Missing Data
by: Koyuncu, Deniz, et al.
Published: (2022)

P2NIA: Privacy-Preserving Non-Iterative Auditing
by: Bourrée, Jade Garcia, et al.
Published: (2025)

Revisiting Ensembling in One-Shot Federated Learning
by: Allouah, Youssef, et al.
Published: (2024)

MissMecha: An All-in-One Python Package for Studying Missing Data Mechanisms
by: Zhou, Youran, et al.
Published: (2025)

Deep Generative Imputation Model for Missing Not At Random Data
by: Chen, Jialei, et al.
Published: (2023)

Active Exploration via Autoregressive Generation of Missing Data
by: Cai, Tiffany Tianhui, et al.
Published: (2024)

Missing Data Imputation using Neural Cellular Automata
by: Luu, Tin, et al.
Published: (2025)