On the Alignment of Group Fairness with Attribute Privacy
Fuente:
arXiv
Saved in:
| Main Authors: | Aalmoes, Jan, Duddu, Vasisht, Boutet, Antoine |
|---|---|
| Format: | Preprint |
| Published: |
2022
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
MixNN: Protection of Federated Learning Against Inference Attacks by Mixing Neural Network Layers
by: Boutet, Antoine, et al.
Published: (2021)
by: Boutet, Antoine, et al.
Published: (2021)
Privacy Bias in Language Models: A Contextual Integrity-based Auditing Metric
by: Shvartzshnaider, Yan, et al.
Published: (2024)
by: Shvartzshnaider, Yan, et al.
Published: (2024)
Locket: Robust Feature-Locking Technique for Language Models
by: He, Lipeng, et al.
Published: (2025)
by: He, Lipeng, et al.
Published: (2025)
Combining Machine Learning Defenses without Conflicts
by: Duddu, Vasisht, et al.
Published: (2024)
by: Duddu, Vasisht, et al.
Published: (2024)
SoK: Unintended Interactions among Machine Learning Defenses and Risks
by: Duddu, Vasisht, et al.
Published: (2023)
by: Duddu, Vasisht, et al.
Published: (2023)
Privacy Assessment of Federated Learning using Private Personalized Layers
by: Jourdan, Théo, et al.
Published: (2021)
by: Jourdan, Théo, et al.
Published: (2021)
Attesting Distributional Properties of Training Data for Machine Learning
by: Duddu, Vasisht, et al.
Published: (2023)
by: Duddu, Vasisht, et al.
Published: (2023)
Towards the Anonymization of the Language Modeling
by: Boutet, Antoine, et al.
Published: (2025)
by: Boutet, Antoine, et al.
Published: (2025)
Machine Learning with Privacy for Protected Attributes
by: Mahloujifar, Saeed, et al.
Published: (2025)
by: Mahloujifar, Saeed, et al.
Published: (2025)
Unified Mechanism-Specific Amplification by Subsampling and Group Privacy Amplification
by: Schuchardt, Jan, et al.
Published: (2024)
by: Schuchardt, Jan, et al.
Published: (2024)
Privacy-Preserving Fair Synthetic Tabular Data
by: Sarmin, Fatima J., et al.
Published: (2025)
by: Sarmin, Fatima J., et al.
Published: (2025)
Optimal Fairness under Local Differential Privacy
by: Ghoukasian, Hrad, et al.
Published: (2025)
by: Ghoukasian, Hrad, et al.
Published: (2025)
FinP: Fairness-in-Privacy in Federated Learning by Addressing Disparities in Privacy Risk
by: Zhao, Tianyu, et al.
Published: (2025)
by: Zhao, Tianyu, et al.
Published: (2025)
PATCH: Mitigating PII Leakage in Language Models with Privacy-Aware Targeted Circuit PatcHing
by: Hughes, Anthony, et al.
Published: (2025)
by: Hughes, Anthony, et al.
Published: (2025)
Laminator: Verifiable ML Property Cards using Hardware-assisted Attestations
by: Duddu, Vasisht, et al.
Published: (2024)
by: Duddu, Vasisht, et al.
Published: (2024)
Privacy for Fairness: Information Obfuscation for Fair Representation Learning with Local Differential Privacy
by: Xie, Songjie, et al.
Published: (2024)
by: Xie, Songjie, et al.
Published: (2024)
FairDP: Certified Fairness with Differential Privacy
by: Tran, Khang, et al.
Published: (2023)
by: Tran, Khang, et al.
Published: (2023)
SAFES: Sequential Privacy and Fairness Enhancing Data Synthesis for Responsible AI
by: Giddens, Spencer, et al.
Published: (2024)
by: Giddens, Spencer, et al.
Published: (2024)
WaKA: Data Attribution using K-Nearest Neighbors and Membership Privacy Principles
by: Mesana, Patrick, et al.
Published: (2024)
by: Mesana, Patrick, et al.
Published: (2024)
VoxGuard: Evaluating User and Attribute Privacy in Speech via Membership Inference Attacks
by: Tsaprazlis, Efthymios, et al.
Published: (2025)
by: Tsaprazlis, Efthymios, et al.
Published: (2025)
A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability
by: Dai, Enyan, et al.
Published: (2022)
by: Dai, Enyan, et al.
Published: (2022)
A Systematic and Formal Study of the Impact of Local Differential Privacy on Fairness: Preliminary Results
by: Makhlouf, Karima, et al.
Published: (2024)
by: Makhlouf, Karima, et al.
Published: (2024)
Optimizing Privacy and Utility Tradeoffs for Group Interests Through Harmonization
by: Mandal, Bishwas, et al.
Published: (2024)
by: Mandal, Bishwas, et al.
Published: (2024)
Amulet: a Python Library for Assessing Interactions Among ML Defenses and Risks
by: Waheed, Asim, et al.
Published: (2025)
by: Waheed, Asim, et al.
Published: (2025)
DP-SGD with weight clipping
by: Barczewski, Antoine, et al.
Published: (2023)
by: Barczewski, Antoine, et al.
Published: (2023)
When Fairness Meets Privacy: Exploring Privacy Threats in Fair Binary Classifiers via Membership Inference Attacks
by: Tian, Huan, et al.
Published: (2023)
by: Tian, Huan, et al.
Published: (2023)
Privacy Constrained Fairness Estimation for Decision Trees
by: van der Steen, Florian, et al.
Published: (2023)
by: van der Steen, Florian, et al.
Published: (2023)
Sampling-Free Privacy Accounting for Matrix Mechanisms under Random Allocation
by: Schuchardt, Jan, et al.
Published: (2026)
by: Schuchardt, Jan, et al.
Published: (2026)
On the Impact of Multi-dimensional Local Differential Privacy on Fairness
by: Makhlouf, Karima, et al.
Published: (2023)
by: Makhlouf, Karima, et al.
Published: (2023)
The Model's Language Matters: A Comparative Privacy Analysis of LLMs
by: Mishra, Abhishek K., et al.
Published: (2025)
by: Mishra, Abhishek K., 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)
by: Arcolezi, Héber H., et al.
Published: (2025)
PAL*M: Property Attestation for Large Generative Models
by: Chantasantitam, Prach, et al.
Published: (2026)
by: Chantasantitam, Prach, et al.
Published: (2026)
Secure Sparse Matrix Multiplications and their Applications to Privacy-Preserving Machine Learning
by: Damie, Marc, et al.
Published: (2025)
by: Damie, Marc, et al.
Published: (2025)
A Differentially Private Kaplan-Meier Estimator for Privacy-Preserving Survival Analysis
by: Veeraragavan, Narasimha Raghavan, et al.
Published: (2024)
by: Veeraragavan, Narasimha Raghavan, et al.
Published: (2024)
Espresso: Robust Concept Filtering in Text-to-Image Models
by: Das, Anudeep, et al.
Published: (2024)
by: Das, Anudeep, et al.
Published: (2024)
Exploring Privacy and Fairness Risks in Sharing Diffusion Models: An Adversarial Perspective
by: Luo, Xinjian, et al.
Published: (2024)
by: Luo, Xinjian, et al.
Published: (2024)
Provable Robustness against Backdoor Attacks via the Primal-Dual Perspective on Differential Privacy
by: Saxena, Aman, et al.
Published: (2026)
by: Saxena, Aman, et al.
Published: (2026)
Credit Attribution and Stable Compression
by: Livni, Roi, et al.
Published: (2024)
by: Livni, Roi, et al.
Published: (2024)
Privacy Amplification by Structured Subsampling for Deep Differentially Private Time Series Forecasting
by: Schuchardt, Jan, et al.
Published: (2025)
by: Schuchardt, Jan, et al.
Published: (2025)
Private Linear Regression with Differential Privacy and PAC Privacy
by: Yang, Hillary, et al.
Published: (2024)
by: Yang, Hillary, et al.
Published: (2024)
Similar Items
-
MixNN: Protection of Federated Learning Against Inference Attacks by Mixing Neural Network Layers
by: Boutet, Antoine, et al.
Published: (2021) -
Privacy Bias in Language Models: A Contextual Integrity-based Auditing Metric
by: Shvartzshnaider, Yan, et al.
Published: (2024) -
Locket: Robust Feature-Locking Technique for Language Models
by: He, Lipeng, et al.
Published: (2025) -
Combining Machine Learning Defenses without Conflicts
by: Duddu, Vasisht, et al.
Published: (2024) -
SoK: Unintended Interactions among Machine Learning Defenses and Risks
by: Duddu, Vasisht, et al.
Published: (2023)