Imagen de Portada

Beyond the Calibration Point: Mechanism Comparison in Differential Privacy

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Kaissis, Georgios, Kolek, Stefan, Balle, Borja, Hayes, Jamie, Rueckert, Daniel
Formato:	Preprint
Publicado:	2024
Materias:	Cryptography and Security Artificial Intelligence Machine Learning Statistics Theory
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Attack-Aware Noise Calibration for Differential Privacy
por: Kulynych, Bogdan, et al.
Publicado: (2024)

Gaussian DP for Reporting Differential Privacy Guarantees in Machine Learning
por: Gomez, Juan Felipe, et al.
Publicado: (2025)

Unifying Re-Identification, Attribute Inference, and Data Reconstruction Risks in Differential Privacy
por: Kulynych, Bogdan, et al.
Publicado: (2025)

Complex-valued Federated Learning with Differential Privacy and MRI Applications
por: Riess, Anneliese, et al.
Publicado: (2021)

Incentivising the federation: gradient-based metrics for data selection and valuation in private decentralised training
por: Usynin, Dmitrii, et al.
Publicado: (2023)

Your Privacy Depends on Others: Collusion Vulnerabilities in Individual Differential Privacy
por: Kaiser, Johannes, et al.
Publicado: (2026)

The Hitchhiker's Guide to Efficient, End-to-End, and Tight DP Auditing
por: Annamalai, Meenatchi Sundaram Muthu Selva, et al.
Publicado: (2025)

Differentially Private Active Learning: Balancing Effective Data Selection and Privacy
por: Schwethelm, Kristian, et al.
Publicado: (2024)

Beyond Covariance Matrix: The Statistical Complexity of Private Linear Regression
por: Chen, Fan, et al.
Publicado: (2025)

On the Privacy of Selection Mechanisms with Gaussian Noise
por: Lebensold, Jonathan, et al.
Publicado: (2024)

Optimality of Staircase Mechanisms for Vector Queries under Differential Privacy
por: Melbourne, James, et al.
Publicado: (2026)

Concentrated Differential Privacy for Bandits
por: Azize, Achraf, et al.
Publicado: (2023)

Beyond Slow Signs in High-fidelity Model Extraction
por: Foerster, Hanna, et al.
Publicado: (2024)

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

A Statistical Viewpoint on Differential Privacy: Hypothesis Testing, Representation and Blackwell's Theorem
por: Su, Weijie J.
Publicado: (2024)

Trusted Machine Learning Models Unlock Private Inference for Problems Currently Infeasible with Cryptography
por: Shumailov, Ilia, et al.
Publicado: (2025)

Mitigating Privacy-Utility Trade-off in Decentralized Federated Learning via $f$-Differential Privacy
por: Li, Xiang, et al.
Publicado: (2025)

Optimal Regret of Bernoulli Bandits under Global Differential Privacy
por: Azize, Achraf, et al.
Publicado: (2025)

Shifted Interpolation for Differential Privacy
por: Bok, Jinho, et al.
Publicado: (2024)

Contraction of Locally Differentially Private Mechanisms
por: Asoodeh, Shahab, et al.
Publicado: (2022)

Differential Privacy Mechanisms in Neural Tangent Kernel Regression
por: Gu, Jiuxiang, et al.
Publicado: (2024)

To Shuffle or not to Shuffle: Auditing DP-SGD with Shuffling
por: Annamalai, Meenatchi Sundaram Muthu Selva, et al.
Publicado: (2024)

RAG with Differential Privacy
por: Grislain, Nicolas
Publicado: (2024)

Differentially Private Manifold Denoising
por: Wu, Jiaqi, et al.
Publicado: (2026)

On Differentially Private U Statistics
por: Chaudhuri, Kamalika, et al.
Publicado: (2024)

Differentially Private Best-Arm Identification
por: Azize, Achraf, et al.
Publicado: (2024)

Counterfactual Influence as a Distributional Quantity
por: Meeus, Matthieu, et al.
Publicado: (2025)

A Statistical Hypothesis Testing Framework for Data Misappropriation Detection in Large Language Models
por: Cai, Yinpeng, et al.
Publicado: (2025)

Privacy of SGD under Gaussian or Heavy-Tailed Noise: Guarantees without Gradient Clipping
por: Şimşekli, Umut, et al.
Publicado: (2024)

Bayesian Adversarial Privacy
por: Bell, Cameron, et al.
Publicado: (2026)

Differentially Private Sliced Inverse Regression: Minimax Optimality and Algorithm
por: Xia, Xintao, et al.
Publicado: (2024)

DP-SPRT: Differentially Private Sequential Probability Ratio Tests
por: Michel, Thomas, et al.
Publicado: (2025)

Beyond Data Privacy: New Privacy Risks for Large Language Models
por: Du, Yuntao, et al.
Publicado: (2025)

Tight Auditing of Differential Privacy in MST and AIM
por: Ganev, Georgi, et al.
Publicado: (2026)

Privacy-preserving machine learning with tensor networks
por: Pozas-Kerstjens, Alejandro, et al.
Publicado: (2022)

Mind the Gap: Mixtures of Gaussians in Approximate Differential Privacy
por: Liu, Huikang, et al.
Publicado: (2026)

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

DPrivBench: Benchmarking LLMs' Reasoning for Differential Privacy
por: Wang, Erchi, et al.
Publicado: (2026)

A Unified Framework for Adversary-Aware Differential Privacy Bounds
por: Swanberg, Marika, et al.
Publicado: (2025)

Differential Privacy-Driven Framework for Enhancing Heart Disease Prediction
por: Otoum, Yazan, et al.
Publicado: (2025)