Image de couverture de livre

Bayesian Pseudo Posterior Mechanism for Differentially Private Machine Learning

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Chew, Robert, Williams, Matthew R., Segarra, Elan A., Preiss, Alexander J., Konet, Amanda, Savitsky, Terrance D.
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

Correlated Noise Mechanisms for Differentially Private Learning
par: Pillutla, Krishna, et autres
Publié: (2025)

Optimal Guarantees for Auditing Rényi Differentially Private Machine Learning
par: Kim, Benjamin D., et autres
Publié: (2026)

Calibrating Practical Privacy Risks for Differentially Private Machine Learning
par: Gu, Yuechun, et autres
Publié: (2024)

DMM: Distributed Matrix Mechanism for Differentially-Private Federated Learning Based on Constant-Overhead Linear Secret Resharing
par: Bienstock, Alexander, et autres
Publié: (2024)

Towards Reliable and Generalizable Differentially Private Machine Learning (Extended Version)
par: Bao, Wenxuan, et autres
Publié: (2025)

Nearly Tight Black-Box Auditing of Differentially Private Machine Learning
par: Annamalai, Meenatchi Sundaram Muthu Selva, et autres
Publié: (2024)

Noise-Aware Differentially Private Regression via Meta-Learning
par: Räisä, Ossi, et autres
Publié: (2024)

Differentially Private Random Feature Model
par: Liao, Chunyang, et autres
Publié: (2024)

Privately Learning Decision Lists and a Differentially Private Winnow
par: Bun, Mark, et autres
Publié: (2026)

Beyond Laplace and Gaussian: Exploring the Generalized Gaussian Mechanism for Private Machine Learning
par: Rinberg, Roy, et autres
Publié: (2025)

DPIS: An Enhanced Mechanism for Differentially Private SGD with Importance Sampling
par: Wei, Jianxin, et autres
Publié: (2022)

Differentially Private Prototypes for Imbalanced Transfer Learning
par: Wahdany, Dariush, et autres
Publié: (2024)

Private and Communication-Efficient Federated Learning based on Differentially Private Sketches
par: Zhang, Meifan, et autres
Publié: (2024)

Differentially Private Selection using Smooth Sensitivity
par: Chaves, Iago, et autres
Publié: (2025)

Q-ShiftDP: A Differentially Private Parameter-Shift Rule for Quantum Machine Learning
par: Ngo, Hoang M., et autres
Publié: (2026)

Differentially Private Training of Mixture of Experts Models
par: Tholoniat, Pierre, et autres
Publié: (2024)

Differentially Private Release and Learning of Threshold Functions
par: Bun, Mark, et autres
Publié: (2015)

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

Differentially Private Learned Indexes
par: Du, Jianzhang, et autres
Publié: (2024)

Advances in Differential Privacy and Differentially Private Machine Learning
par: Das, Saswat, et autres
Publié: (2024)

Privacy-Preserving In-Context Learning with Differentially Private Few-Shot Generation
par: Tang, Xinyu, et autres
Publié: (2023)

Oracle-Efficient Differentially Private Learning with Public Data
par: Block, Adam, et autres
Publié: (2024)

Differentially Private Federated Learning: A Systematic Review
par: Fu, Jie, et autres
Publié: (2024)

Universal Exact Compression of Differentially Private Mechanisms
par: Liu, Yanxiao, et autres
Publié: (2024)

Correlated Privacy Mechanisms for Differentially Private Distributed Mean Estimation
par: Vithana, Sajani, et autres
Publié: (2024)

Scalable Differential Privacy Mechanisms for Real-Time Machine Learning Applications
par: Smith, Jessica, et autres
Publié: (2024)

Delving into Differentially Private Transformer
par: Ding, Youlong, et autres
Publié: (2024)

Differentially Private Attention Computation
par: Gao, Yeqi, et autres
Publié: (2023)

Differentially Private Diffusion Models
par: Dockhorn, Tim, et autres
Publié: (2022)

Differentially Private Relational Learning with Entity-level Privacy Guarantees
par: Huang, Yinan, et autres
Publié: (2025)

DPDR: Gradient Decomposition and Reconstruction for Differentially Private Deep Learning
par: Liu, Yixuan, et autres
Publié: (2024)

Differentially Private Log-Location-Scale Regression Using Functional Mechanism
par: Sheng, Jiewen, et autres
Publié: (2024)

Provable Differentially Private Computation of the Cross-Attention Mechanism
par: Ke, Yekun, et autres
Publié: (2024)

Graphical vs. Deep Generative Models: Measuring the Impact of Differentially Private Mechanisms and Budgets on Utility
par: Ganev, Georgi, et autres
Publié: (2023)

Differentially Private Active Learning: Balancing Effective Data Selection and Privacy
par: Schwethelm, Kristian, et autres
Publié: (2024)

Differentially Private Federated Learning without Noise Addition: When is it Possible?
par: Zhang, Jiang, et autres
Publié: (2024)

PrivORL: Differentially Private Synthetic Dataset for Offline Reinforcement Learning
par: Gong, Chen, et autres
Publié: (2025)

ALI-DPFL: Differentially Private Federated Learning with Adaptive Local Iterations
par: Ling, Xinpeng, et autres
Publié: (2023)

Position: Considerations for Differentially Private Learning with Large-Scale Public Pretraining
par: Tramèr, Florian, et autres
Publié: (2022)

Differentially Private Conditional Independence Testing
par: Kalemaj, Iden, et autres
Publié: (2023)