Imagen de Portada

The Limits of Differential Privacy in Online Learning

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Li, Bo, Wang, Wei, Ye, Peng
Formato:	Preprint
Publicado:	2024
Materias:	Machine Learning
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Improved Bounds for Pure Private Agnostic Learning: Item-Level and User-Level Privacy
por: Li, Bo, et al.
Publicado: (2024)

Private Online Learning against an Adaptive Adversary: Realizable and Agnostic Settings
por: Li, Bo, et al.
Publicado: (2025)

On Theoretical Limits of Learning with Label Differential Privacy
por: Zhao, Puning, et al.
Publicado: (2025)

Forward Learning with Differential Privacy
por: Feng, Mingqian, et al.
Publicado: (2025)

Limits of Personalizing Differential Privacy Budgets
por: Cyffers, Edwige, et al.
Publicado: (2026)

Federated Transfer Learning with Differential Privacy
por: Li, Mengchu, et al.
Publicado: (2024)

Adversarial Signed Graph Learning with Differential Privacy
por: Ke, Haobin, et al.
Publicado: (2025)

Private Realizable-to-Agnostic Transformation with Near-Optimal Sample Complexity
por: Li, Bo, et al.
Publicado: (2025)

Learning with User-Level Local Differential Privacy
por: Zhao, Puning, et al.
Publicado: (2024)

Online Convex Optimization with Memory and Limited Predictions
por: Wang, Zhengmiao, et al.
Publicado: (2024)

Improved Regret in Stochastic Decision-Theoretic Online Learning under Differential Privacy
por: Wu, Ruihan, et al.
Publicado: (2025)

Differentially Private Relational Learning with Entity-level Privacy Guarantees
por: Huang, Yinan, et al.
Publicado: (2025)

Poincaré Differential Privacy for Hierarchy-Aware Graph Embedding
por: Wei, Yuecen, et al.
Publicado: (2023)

Beyond Membership: Limitations of Add/Remove Adjacency in Differential Privacy
por: Pradhan, Gauri, et al.
Publicado: (2025)

Optimizing Communication and Device Clustering for Clustered Federated Learning with Differential Privacy
por: Wei, Dongyu, et al.
Publicado: (2025)

Enhancing Learning with Label Differential Privacy by Vector Approximation
por: Zhao, Puning, et al.
Publicado: (2024)

Offline and Online KL-Regularized RLHF under Differential Privacy
por: Wu, Yulian, et al.
Publicado: (2025)

Consistent Estimation of Numerical Distributions under Local Differential Privacy by Wavelet Expansion
por: Zhao, Puning, et al.
Publicado: (2025)

Cape: Context-Aware Prompt Perturbation Mechanism with Differential Privacy
por: Wu, Haoqi, et al.
Publicado: (2025)

Efficient Federated Unlearning with Adaptive Differential Privacy Preservation
por: Jiang, Yu, et al.
Publicado: (2024)

Tractable MCMC for Private Learning with Pure and Gaussian Differential Privacy
por: Lin, Yingyu, et al.
Publicado: (2023)

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

Local Differential Privacy is Not Enough: A Sample Reconstruction Attack against Federated Learning with Local Differential Privacy
por: You, Zhichao, et al.
Publicado: (2025)

On Evaluating the Poisoning Robustness of Federated Learning under Local Differential Privacy
por: Wang, Zijian, et al.
Publicado: (2025)

DDP-SA: Scalable Privacy-Preserving Federated Learning via Distributed Differential Privacy and Secure Aggregation
por: Wei, Wenjing, et al.
Publicado: (2026)

A Privacy-Preserving Framework for Advertising Personalization Incorporating Federated Learning and Differential Privacy
por: Li, Xiang, et al.
Publicado: (2025)

Near-Optimal Reinforcement Learning with Shuffle Differential Privacy
por: Bai, Shaojie, et al.
Publicado: (2024)

Convergent Differential Privacy Analysis for General Federated Learning
por: Sun, Yan, et al.
Publicado: (2024)

Statistical Limits and Efficient Algorithms for Differentially Private Federated Learning
por: Auddy, Arnab, et al.
Publicado: (2026)

When Differential Privacy Meets Wireless Federated Learning: An Improved Analysis for Privacy and Convergence
por: Yaoling, Chen, et al.
Publicado: (2026)

Lower Bounds for Differential Privacy Under Continual Observation and Online Threshold Queries
por: Cohen, Edith, et al.
Publicado: (2024)

Online Sensitivity Optimization in Differentially Private Learning
por: Galli, Filippo, et al.
Publicado: (2023)

Federated Hypergraph Learning with Local Differential Privacy: Toward Privacy-Aware Hypergraph Structure Completion
por: Luo, Linfeng, et al.
Publicado: (2024)

Federated Cubic Regularized Newton Learning with Sparsification-amplified Differential Privacy
por: Huo, Wei, et al.
Publicado: (2024)

Efficient Online Reinforcement Learning for Diffusion Policy
por: Ma, Haitong, et al.
Publicado: (2025)

The Power of Bias: Optimizing Client Selection in Federated Learning with Heterogeneous Differential Privacy
por: Ma, Jiating, et al.
Publicado: (2024)

Communication-Efficient Distributed Learning with Differential Privacy
por: Ren, Xiaoxing, et al.
Publicado: (2026)

Federated Online Prediction from Experts with Differential Privacy: Separations and Regret Speed-ups
por: Gao, Fengyu, et al.
Publicado: (2024)

Privacy-Preserving Federated Learning via Differential Privacy and Homomorphic Encryption for Cardiovascular Disease Risk Modeling
por: Sharma, Gaurang, et al.
Publicado: (2026)

Privacy for Fairness: Information Obfuscation for Fair Representation Learning with Local Differential Privacy
por: Xie, Songjie, et al.
Publicado: (2024)