Copertina

On the Impact of Multi-dimensional Local Differential Privacy on Fairness

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Makhlouf, Karima, Arcolezi, Heber H., Zhioua, Sami, Brahim, Ghassen Ben, Palamidessi, Catuscia
Natura:	Preprint
Pubblicazione:	2023
Soggetti:	Machine Learning Cryptography and Security Computers and Society
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

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

Causal Discovery Under Local Privacy
di: Binkytė, Rūta, et al.
Pubblicazione: (2023)

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

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

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

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

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

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

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

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

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

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

On the Privacy of LLMs: An Ablation Study
di: Makhlouf, Karima, et al.
Pubblicazione: (2026)

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

Privacy Constrained Fairness Estimation for Decision Trees
di: van der Steen, Florian, et al.
Pubblicazione: (2023)

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

Personalized Differential Privacy for Ridge Regression
di: Acharya, Krishna, et al.
Pubblicazione: (2024)

Differentially Private Post-Processing for Fair Regression
di: Xian, Ruicheng, et al.
Pubblicazione: (2024)

Privacy at a Price: Exploring its Dual Impact on AI Fairness
di: Yang, Mengmeng, et al.
Pubblicazione: (2024)

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

Position Paper: Assessing Robustness, Privacy, and Fairness in Federated Learning Integrated with Foundation Models
di: Wang, Jiaqi, et al.
Pubblicazione: (2024)

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

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

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

LDPKiT: Superimposing Remote Queries for Privacy-Preserving Local Model Training
di: Li, Kexin, et al.
Pubblicazione: (2024)

Exploring the Integration of Differential Privacy in Cybersecurity Analytics: Balancing Data Utility and Privacy in Threat Intelligence
di: Sedraoui, Brahim Khalil, et al.
Pubblicazione: (2026)

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

PUFFLE: Balancing Privacy, Utility, and Fairness in Federated Learning
di: Corbucci, Luca, et al.
Pubblicazione: (2024)

A Survey on Differential Privacy for SpatioTemporal Data in Transportation Research
di: Bhadani, Rahul
Pubblicazione: (2024)

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

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

De-amplifying Bias from Differential Privacy in Language Model Fine-tuning
di: Srivastava, Sanjari, et al.
Pubblicazione: (2024)

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

Information Leakage Envelopes
di: Saeidian, Sara, et al.
Pubblicazione: (2026)

Privacy-Preserving Dataset Combination
di: Fuentes, Keren, et al.
Pubblicazione: (2025)

Learning Fair Robustness via Domain Mixup
di: Zhong, Meiyu, et al.
Pubblicazione: (2024)

Prediction with Expert Advice under Local Differential Privacy
di: Jacobsen, Ben, et al.
Pubblicazione: (2025)

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

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

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