Imagen de Portada

Tight Differential Privacy Guarantees for the Shuffle Model with $k$-Randomized Response

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Biswas, Sayan, Jung, Kangsoo, Palamidessi, Catuscia
Formato:	Preprint
Publicado:	2022
Materias:	Cryptography and Security
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Mitigating Membership Inference Vulnerability in Personalized Federated Learning
por: Jung, Kangsoo, et al.
Publicado: (2025)

Protection against Source Inference Attacks in Federated Learning using Unary Encoding and Shuffling
por: Athanasiou, Andreas, et al.
Publicado: (2024)

Protection against Source Inference Attacks in Federated Learning
por: Athanasiou, Andreas, et al.
Publicado: (2026)

A privacy preserving querying mechanism with high utility for electric vehicles
por: Atmaca, Ugur Ilker, et al.
Publicado: (2022)

Metric Privacy in Federated Learning for Medical Imaging: Improving Convergence and Preventing Client Inference Attacks
por: Díaz, Judith Sáinz-Pardo, et al.
Publicado: (2025)

Causal Discovery Under Local Privacy
por: Binkytė, Rūta, et al.
Publicado: (2023)

A Systematic and Formal Study of the Impact of Local Differential Privacy on Fairness: Preliminary Results
por: Makhlouf, Karima, et al.
Publicado: (2024)

Beyond Epsilon: A Principled QIF Framework for Local Differential Privacy
por: Gonze, Ramon G., et al.
Publicado: (2026)

Jeffrey's update rule as a minimizer of Kullback-Leibler divergence
por: Pinzón, Carlos, et al.
Publicado: (2025)

Self-Defense: Optimal QIF Solutions and Application to Website Fingerprinting
por: Athanasiou, Andreas, et al.
Publicado: (2024)

On the Consistency and Performance of the Iterative Bayesian Update
por: ElSalamouny, Ehab, et al.
Publicado: (2025)

Estimating the True Distribution of Data Collected with Randomized Response
por: Pinzón, Carlos Antonio, et al.
Publicado: (2026)

On the Impact of Multi-dimensional Local Differential Privacy on Fairness
por: Makhlouf, Karima, et al.
Publicado: (2023)

Information Leakage Envelopes
por: Saeidian, Sara, et al.
Publicado: (2026)

Bayes Security: A Not So Average Metric
por: Chatzikokolakis, Konstantinos, et al.
Publicado: (2020)

Comparing privacy notions for protection against reconstruction attacks in machine learning
por: Biswas, Sayan, et al.
Publicado: (2025)

Bayes' capacity as a measure for reconstruction attacks in federated learning
por: Biswas, Sayan, et al.
Publicado: (2024)

Efficient Verifiable Differential Privacy with Input Authenticity in the Local and Shuffle Model
por: Bontekoe, Tariq, et al.
Publicado: (2024)

RAIN: Secure and Robust Aggregation under Shuffle Model of Differential Privacy
por: Li, Yuhang, et al.
Publicado: (2026)

A Generalized Shuffle Framework for Privacy Amplification: Strengthening Privacy Guarantees and Enhancing Utility
por: Chen, E, et al.
Publicado: (2023)

Camel: Communication-Efficient and Maliciously Secure Federated Learning in the Shuffle Model of Differential Privacy
por: Xu, Shuangqing, et al.
Publicado: (2024)

Verifiable Differential Privacy
por: Biswas, Ari, et al.
Publicado: (2022)

Robust Single-message Shuffle Differential Privacy Protocol for Accurate Distribution Estimation
por: Li, Xiaoguang, et al.
Publicado: (2026)

Augmented Shuffle Protocols for Accurate and Robust Frequency Estimation under Differential Privacy
por: Murakami, Takao, et al.
Publicado: (2025)

Bipartite Randomized Response Mechanism for Local Differential Privacy
por: Zhang, Shun, et al.
Publicado: (2025)

Efficient Fault-Tolerant Quantum Protocol for Differential Privacy in the Shuffle Model
por: Asghar, Hassan Jameel, et al.
Publicado: (2024)

Enhanced Privacy Bound for Shuffle Model with Personalized Privacy
por: Liu, Yixuan, et al.
Publicado: (2024)

Augmented Shuffle Differential Privacy Protocols for Large-Domain Categorical and Key-Value Data
por: Murakami, Takao, et al.
Publicado: (2025)

A New Analysis of Differential Privacy's Generalization Guarantees
por: Jung, Christopher, et al.
Publicado: (2019)

Privacy Guarantees for Personal Mobility Data in Humanitarian Response
por: Kohli, Nitin, et al.
Publicado: (2023)

Almost Instance-optimal Clipping for Summation Problems in the Shuffle Model of Differential Privacy
por: Dong, Wei, et al.
Publicado: (2024)

Trade-off Functions for DP-SGD with Subsampling based on Random Shuffling: Tight Upper and Lower Bounds
por: van Dijk, Marten, et al.
Publicado: (2026)

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

Privacy Amplification via Shuffling: Unified, Simplified, and Tightened
por: Wang, Shaowei, et al.
Publicado: (2023)

Decomposition-Based Optimal Bounds for Privacy Amplification via Shuffling
por: Su, Pengcheng, et al.
Publicado: (2025)

From Randomized Response to Randomized Index: Answering Subset Counting Queries with Local Differential Privacy
por: Ye, Qingqing, et al.
Publicado: (2025)

Tight Privacy Audit in One Run
por: Xiang, Zihang, et al.
Publicado: (2025)

Auditing Differential Privacy Guarantees Using Density Estimation
por: Koskela, Antti, et al.
Publicado: (2024)

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

Differential Privacy in Federated Learning: Mitigating Inference Attacks with Randomized Response
por: Ozturk, Ozer, et al.
Publicado: (2025)