Sliced Rényi Pufferfish Privacy: Directional Additive Noise Mechanism and Private Learning with Gradient Clipping
Fuente:
arXiv
Guardado en:
| Autores principales: | Zhang, Tao, Vorobeychik, Yevgeniy |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Rényi Pufferfish Privacy: General Additive Noise Mechanisms and Privacy Amplification by Iteration
por: Pierquin, Clément, et al.
Publicado: (2023)
por: Pierquin, Clément, et al.
Publicado: (2023)
Residual-PAC Privacy: Automatic Privacy Control Beyond the Gaussian Barrier
por: Zhang, Tao, et al.
Publicado: (2025)
por: Zhang, Tao, et al.
Publicado: (2025)
$α$-Wasserstein Mechanism for Rényi Pufferfish Privacy
por: Ding, Ni, et al.
Publicado: (2026)
por: Ding, Ni, et al.
Publicado: (2026)
Differential Confounding Privacy and Inverse Composition
por: Zhang, Tao, et al.
Publicado: (2024)
por: Zhang, Tao, et al.
Publicado: (2024)
Rényi Pufferfish Privacy with Gaussian-based Priors: From Single Gaussian to Mixture Model
por: Yang, Wenjin, et al.
Publicado: (2026)
por: Yang, Wenjin, et al.
Publicado: (2026)
Noise Reduction for Pufferfish Privacy: A Practical Noise Calibration Method
por: Yang, Wenjin, et al.
Publicado: (2026)
por: Yang, Wenjin, et al.
Publicado: (2026)
Composition for Pufferfish Privacy
por: Bai, Jiamu, et al.
Publicado: (2026)
por: Bai, Jiamu, et al.
Publicado: (2026)
Multi-user Pufferfish Privacy
por: Ding, Ni, et al.
Publicado: (2025)
por: Ding, Ni, et al.
Publicado: (2025)
Bayes-Nash Generative Privacy Against Membership Inference Attacks
por: Zhang, Tao, et al.
Publicado: (2024)
por: Zhang, Tao, et al.
Publicado: (2024)
A Game-Theoretic Approach to Privacy-Utility Tradeoff in Sharing Genomic Summary Statistics
por: Zhang, Tao, et al.
Publicado: (2024)
por: Zhang, Tao, et al.
Publicado: (2024)
A Scalable Approach to Solving Simulation-Based Network Security Games
por: Lanier, Michael, et al.
Publicado: (2026)
por: Lanier, Michael, et al.
Publicado: (2026)
Adversarial Reinforcement Learning for Detecting False Data Injection Attacks in Vehicular Routing
por: Eghtesad, Taha, et al.
Publicado: (2026)
por: Eghtesad, Taha, et al.
Publicado: (2026)
CyGym: A Simulation-Based Game-Theoretic Analysis Framework for Cybersecurity
por: Lanier, Michael, et al.
Publicado: (2025)
por: Lanier, Michael, et al.
Publicado: (2025)
Approximation of Pufferfish Privacy for Gaussian Priors
por: Ding, Ni
Publicado: (2024)
por: Ding, Ni
Publicado: (2024)
Adversarial Machine Unlearning
por: Di, Zonglin, et al.
Publicado: (2024)
por: Di, Zonglin, et al.
Publicado: (2024)
Multi-Agent Reinforcement Learning for Assessing False-Data Injection Attacks on Transportation Networks
por: Eghtesad, Taha, et al.
Publicado: (2023)
por: Eghtesad, Taha, et al.
Publicado: (2023)
AgentDyn: Are Your Agent Security Defenses Deployable in Real-World Dynamic Environments?
por: Li, Hao, et al.
Publicado: (2026)
por: Li, Hao, et al.
Publicado: (2026)
Privacy without Noisy Gradients: Slicing Mechanism for Generative Model Training
por: Greenewald, Kristjan, et al.
Publicado: (2024)
por: Greenewald, Kristjan, et al.
Publicado: (2024)
Low Rank Adaptation for Adversarial Perturbation
por: Liu, Han, et al.
Publicado: (2026)
por: Liu, Han, et al.
Publicado: (2026)
Privacy of SGD under Gaussian or Heavy-Tailed Noise: Guarantees without Gradient Clipping
por: Şimşekli, Umut, et al.
Publicado: (2024)
por: Şimşekli, Umut, et al.
Publicado: (2024)
Enabling Differentially Private Federated Learning for Speech Recognition: Benchmarks, Adaptive Optimizers and Gradient Clipping
por: Pelikan, Martin, et al.
Publicado: (2023)
por: Pelikan, Martin, et al.
Publicado: (2023)
Enhanced Privacy Leakage from Noise-Perturbed Gradients via Gradient-Guided Conditional Diffusion Models
por: Meng, Jiayang, et al.
Publicado: (2025)
por: Meng, Jiayang, et al.
Publicado: (2025)
Differentially Private Gradient Flow based on the Sliced Wasserstein Distance
por: Sebag, Ilana, et al.
Publicado: (2023)
por: Sebag, Ilana, et al.
Publicado: (2023)
Correlated Noise Mechanisms for Differentially Private Learning
por: Pillutla, Krishna, et al.
Publicado: (2025)
por: Pillutla, Krishna, et al.
Publicado: (2025)
Attacks on Node Attributes in Graph Neural Networks
por: Xu, Ying, et al.
Publicado: (2024)
por: Xu, Ying, et al.
Publicado: (2024)
Quantum Pufferfish Privacy: A Flexible Privacy Framework for Quantum Systems
por: Nuradha, Theshani, et al.
Publicado: (2023)
por: Nuradha, Theshani, et al.
Publicado: (2023)
Optimal conversion from Rényi Differential Privacy to $f$-Differential Privacy
por: Riess, Anneliese, et al.
Publicado: (2026)
por: Riess, Anneliese, et al.
Publicado: (2026)
Calibrating Noise for Group Privacy in Subsampled Mechanisms
por: Jiang, Yangfan, et al.
Publicado: (2024)
por: Jiang, Yangfan, et al.
Publicado: (2024)
GeoClip: Geometry-Aware Clipping for Differentially Private SGD
por: Gilani, Atefeh, et al.
Publicado: (2025)
por: Gilani, Atefeh, et al.
Publicado: (2025)
Accuracy-First Rényi Differential Privacy and Post-Processing Immunity
por: Räisä, Ossi, et al.
Publicado: (2025)
por: Räisä, Ossi, et al.
Publicado: (2025)
DOPPLER: Differentially Private Optimizers with Low-pass Filter for Privacy Noise Reduction
por: Zhang, Xinwei, et al.
Publicado: (2024)
por: Zhang, Xinwei, et al.
Publicado: (2024)
Measured Hockey-Stick Divergence and its Applications to Quantum Pufferfish Privacy
por: Nuradha, Theshani, et al.
Publicado: (2025)
por: Nuradha, Theshani, et al.
Publicado: (2025)
Differentially Private SGD Without Clipping Bias: An Error-Feedback Approach
por: Zhang, Xinwei, et al.
Publicado: (2023)
por: Zhang, Xinwei, et al.
Publicado: (2023)
RLHFPoison: Reward Poisoning Attack for Reinforcement Learning with Human Feedback in Large Language Models
por: Wang, Jiongxiao, et al.
Publicado: (2023)
por: Wang, Jiongxiao, et al.
Publicado: (2023)
Advances in Differential Privacy and Differentially Private Machine Learning
por: Das, Saswat, et al.
Publicado: (2024)
por: Das, Saswat, et al.
Publicado: (2024)
On the Privacy of Selection Mechanisms with Gaussian Noise
por: Lebensold, Jonathan, et al.
Publicado: (2024)
por: Lebensold, Jonathan, et al.
Publicado: (2024)
DC-SGD: Differentially Private SGD with Dynamic Clipping through Gradient Norm Distribution Estimation
por: Wei, Chengkun, et al.
Publicado: (2025)
por: Wei, Chengkun, et al.
Publicado: (2025)
Optimal Guarantees for Auditing Rényi Differentially Private Machine Learning
por: Kim, Benjamin D., et al.
Publicado: (2026)
por: Kim, Benjamin D., et al.
Publicado: (2026)
Enhancing Accuracy-Privacy Trade-off in Differentially Private Split Learning
por: Pham, Ngoc Duy, et al.
Publicado: (2023)
por: Pham, Ngoc Duy, et al.
Publicado: (2023)
Dobrushin Coefficients of Private Mechanisms Beyond Local Differential Privacy
por: Grosse, Leonhard, et al.
Publicado: (2026)
por: Grosse, Leonhard, et al.
Publicado: (2026)
Ejemplares similares
-
Rényi Pufferfish Privacy: General Additive Noise Mechanisms and Privacy Amplification by Iteration
por: Pierquin, Clément, et al.
Publicado: (2023) -
Residual-PAC Privacy: Automatic Privacy Control Beyond the Gaussian Barrier
por: Zhang, Tao, et al.
Publicado: (2025) -
$α$-Wasserstein Mechanism for Rényi Pufferfish Privacy
por: Ding, Ni, et al.
Publicado: (2026) -
Differential Confounding Privacy and Inverse Composition
por: Zhang, Tao, et al.
Publicado: (2024) -
Rényi Pufferfish Privacy with Gaussian-based Priors: From Single Gaussian to Mixture Model
por: Yang, Wenjin, et al.
Publicado: (2026)