Image de couverture de livre

Privacy Amplification Through Synthetic Data: Insights from Linear Regression

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Pierquin, Clément, Bellet, Aurélien, Tommasi, Marc, Boussard, Matthieu
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

Privacy Amplification Persists under Unlimited Synthetic Data Release
par: Pierquin, Clément, et autres
Publié: (2026)

Rényi Pufferfish Privacy: General Additive Noise Mechanisms and Privacy Amplification by Iteration
par: Pierquin, Clément, et autres
Publié: (2023)

Muffliato: Peer-to-Peer Privacy Amplification for Decentralized Optimization and Averaging
par: Cyffers, Edwige, et autres
Publié: (2022)

Privacy Attacks in Decentralized Learning
par: Mrini, Abdellah El, et autres
Publié: (2024)

Tighter Privacy Auditing of DP-SGD in the Hidden State Threat Model
par: Cebere, Tudor, et autres
Publié: (2024)

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

Byzantine Failures Harm the Generalization of Robust Distributed Learning Algorithms More Than Data Poisoning
par: Boudou, Thomas, et autres
Publié: (2025)

On the Statistical Complexity of Estimation and Testing under Privacy Constraints
par: Lalanne, Clément, et autres
Publié: (2022)

Private Linear Regression with Differential Privacy and PAC Privacy
par: Yang, Hillary, et autres
Publié: (2024)

Leveraging Randomness in Model and Data Partitioning for Privacy Amplification
par: Dong, Andy, et autres
Publié: (2025)

The Relative Gaussian Mechanism and its Application to Private Gradient Descent
par: Hendrikx, Hadrien, et autres
Publié: (2023)

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

Unified Mechanism-Specific Amplification by Subsampling and Group Privacy Amplification
par: Schuchardt, Jan, et autres
Publié: (2024)

Private Rate-Constrained Optimization with Applications to Fair Learning
par: Yaghini, Mohammad, et autres
Publié: (2025)

Personalized Privacy Amplification via Importance Sampling
par: Fay, Dominik, et autres
Publié: (2023)

Privacy-Preserving Fair Synthetic Tabular Data
par: Sarmin, Fatima J., et autres
Publié: (2025)

Privacy Vulnerabilities in Marginals-based Synthetic Data
par: Golob, Steven, et autres
Publié: (2024)

Privacy Amplification for the Gaussian Mechanism via Bounded Support
par: Hu, Shengyuan, et autres
Publié: (2024)

Understanding the Impact of Data Domain Extraction on Synthetic Data Privacy
par: Ganev, Georgi, et autres
Publié: (2025)

Does Training with Synthetic Data Truly Protect Privacy?
par: Zhao, Yunpeng, et autres
Publié: (2025)

Synthetic Data: Revisiting the Privacy-Utility Trade-off
par: Sarmin, Fatima Jahan, et autres
Publié: (2024)

Learning with Locally Private Examples by Inverse Weierstrass Private Stochastic Gradient Descent
par: Dufraiche, Jean, et autres
Publié: (2026)

SMOTE-DP: Improving Privacy-Utility Tradeoff with Synthetic Data
par: Zhou, Yan, et autres
Publié: (2025)

Scalable and Privacy-Preserving Synthetic Data Generation on Decentralised Web
par: Ramesh, Vishal, et autres
Publié: (2023)

Privacy Amplification by Structured Subsampling for Deep Differentially Private Time Series Forecasting
par: Schuchardt, Jan, et autres
Publié: (2025)

Privacy-aware Gaussian Process Regression
par: Tuo, Rui, et autres
Publié: (2023)

The Canary's Echo: Auditing Privacy Risks of LLM-Generated Synthetic Text
par: Meeus, Matthieu, et autres
Publié: (2025)

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

SafeSynthDP: Leveraging Large Language Models for Privacy-Preserving Synthetic Data Generation Using Differential Privacy
par: Nahid, Md Mahadi Hasan, et autres
Publié: (2024)

Breaking Agents: Compromising Autonomous LLM Agents Through Malfunction Amplification
par: Zhang, Boyang, et autres
Publié: (2024)

WaKA: Data Attribution using K-Nearest Neighbors and Membership Privacy Principles
par: Mesana, Patrick, et autres
Publié: (2024)

Privacy Auditing with Zero (0) Training Run
par: Cebere, Tudor, et autres
Publié: (2026)

DP-SGD Without Clipping: The Lipschitz Neural Network Way
par: Bethune, Louis, et autres
Publié: (2023)

A Linear Reconstruction Approach for Attribute Inference Attacks against Synthetic Data
par: Annamalai, Meenatchi Sundaram Muthu Selva, et autres
Publié: (2023)

Adaptive Methods Are Preferable in High Privacy Settings: An SDE Perspective
par: Compagnoni, Enea Monzio, et autres
Publié: (2026)

Driving Privacy Forward: Mitigating Information Leakage within Smart Vehicles through Synthetic Data Generation
par: Parikh, Krish
Publié: (2024)

Detecting Adversarial Data via Provable Adversarial Noise Amplification
par: Mumcu, Furkan, et autres
Publié: (2026)

Privacy in Theory, Bugs in Practice: Grey-Box Auditing of Differential Privacy Libraries
par: Cebere, Tudor, et autres
Publié: (2026)

The DCR Delusion: Measuring the Privacy Risk of Synthetic Data
par: Yao, Zexi, et autres
Publié: (2025)

Bounding the Excess Risk for Linear Models Trained on Marginal-Preserving, Differentially-Private, Synthetic Data
par: Zhou, Yvonne, et autres
Publié: (2024)