Cover Image

A Systematic and Formal Study of the Impact of Local Differential Privacy on Fairness: Preliminary Results

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Makhlouf, Karima, Stefanovic, Tamara, Arcolezi, Heber H., Palamidessi, Catuscia
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Cryptography and Security
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

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

Causal Discovery Under Local Privacy
by: Binkytė, Rūta, et al.
Published: (2023)

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

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

Fair Play for Individuals, Foul Play for Groups? Auditing Anonymization's Impact on ML Fairness
by: Arcolezi, Héber H., et al.
Published: (2025)

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

Revisiting Locally Differentially Private Protocols: Towards Better Trade-offs in Privacy, Utility, and Attack Resistance
by: Arcolezi, Héber H., et al.
Published: (2025)

Tight Differential Privacy Guarantees for the Shuffle Model with $k$-Randomized Response
by: Biswas, Sayan, et al.
Published: (2022)

Revealing the True Cost of Locally Differentially Private Protocols: An Auditing Perspective
by: Arcolezi, Héber H., et al.
Published: (2023)

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

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

Optimal Fairness under Local Differential Privacy
by: Ghoukasian, Hrad, et al.
Published: (2025)

Private Frequency Estimation Via Residue Number Systems
by: Arcolezi, Héber H.
Published: (2025)

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

From Mean to Extreme: Formal Differential Privacy Bounds on the Success of Real-World Data Reconstruction Attacks
by: Riess, Anneliese, et al.
Published: (2024)

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

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

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

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

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

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

FairDP: Certified Fairness with Differential Privacy
by: Tran, Khang, et al.
Published: (2023)

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

Towards Explainable Federated Learning: Understanding the Impact of Differential Privacy
by: Oliveira, Júlio, et al.
Published: (2026)

Information Leakage Envelopes
by: Saeidian, Sara, et al.
Published: (2026)

Understanding Disclosure Risk in Differential Privacy with Applications to Noise Calibration and Auditing (Extended Version)
by: Guerra-Balboa, Patricia, et al.
Published: (2026)

Poisoning Attacks to Local Differential Privacy Protocols for Trajectory Data
by: Hsu, I-Jung, et al.
Published: (2025)

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

Local Differential Privacy in Graph Neural Networks: a Reconstruction Approach
by: Bhaila, Karuna, et al.
Published: (2023)

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

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

How Tough Is Location Anonymization? Re-identifying 100K Real-User Trajectories in Japan
by: Mishra, Abhishek Kumar, et al.
Published: (2025)

On the Alignment of Group Fairness with Attribute Privacy
by: Aalmoes, Jan, et al.
Published: (2022)

Wasserstein Differential Privacy
by: Yang, Chengyi, et al.
Published: (2024)

Privacy-Preserving Fair Synthetic Tabular Data
by: Sarmin, Fatima J., et al.
Published: (2025)

Degree-Preserving Randomized Response for Graph Neural Networks under Local Differential Privacy
by: Hidano, Seira, et al.
Published: (2022)

FinP: Fairness-in-Privacy in Federated Learning by Addressing Disparities in Privacy Risk
by: Zhao, Tianyu, et al.
Published: (2025)

Private Linear Regression with Differential Privacy and PAC Privacy
by: Yang, Hillary, et al.
Published: (2024)

Jacobian-Guided Anisotropic Noise Reshaping for Enhancing Representation Utility under Local Differential Privacy
by: Ha, Youngmok, et al.
Published: (2026)

Laplace Transform Interpretation of Differential Privacy
by: Chourasia, Rishav, et al.
Published: (2024)