Saved in:
| Main Authors: | Busa-Fekete, Róbert István, Dick, Travis, Gentile, Claudio, Medina, Andrés Muñoz, Smith, Adam, Swanberg, Marika |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2406.02797 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Privacy in Metalearning and Multitask Learning: Modeling and Separations
by: Aliakbarpour, Maryam, et al.
Published: (2024)
by: Aliakbarpour, Maryam, et al.
Published: (2024)
The Sample Complexity of Membership Inference and Privacy Auditing
by: Haghifam, Mahdi, et al.
Published: (2025)
by: Haghifam, Mahdi, et al.
Published: (2025)
A Unified Framework for Adversary-Aware Differential Privacy Bounds
by: Swanberg, Marika, et al.
Published: (2025)
by: Swanberg, Marika, et al.
Published: (2025)
Observational Auditing of Label Privacy
by: Kalemaj, Iden, et al.
Published: (2025)
by: Kalemaj, Iden, et al.
Published: (2025)
ATTAXONOMY: Unpacking Differential Privacy Guarantees Against Practical Adversaries
by: Cummings, Rachel, et al.
Published: (2024)
by: Cummings, Rachel, et al.
Published: (2024)
Is API Access to LLMs Useful for Generating Private Synthetic Tabular Data?
by: Swanberg, Marika, et al.
Published: (2025)
by: Swanberg, Marika, et al.
Published: (2025)
Privacy Auditing of Multi-domain Graph Pre-trained Model under Membership Inference Attacks
by: Luo, Jiayi, et al.
Published: (2025)
by: Luo, Jiayi, et al.
Published: (2025)
KDk: A Defense Mechanism Against Label Inference Attacks in Vertical Federated Learning
by: Arazzi, Marco, et al.
Published: (2024)
by: Arazzi, Marco, et al.
Published: (2024)
Black-Box Privacy Attacks on Shared Representations in Multitask Learning
by: Abascal, John, et al.
Published: (2025)
by: Abascal, John, et al.
Published: (2025)
Enhancing One-run Privacy Auditing with Quantile Regression-Based Membership Inference
by: Liu, Terrance, et al.
Published: (2025)
by: Liu, Terrance, et al.
Published: (2025)
Differentially Private Synthetic Data Release for Topics API Outputs
by: Dick, Travis, et al.
Published: (2025)
by: Dick, Travis, et al.
Published: (2025)
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)
OSLO: One-Shot Label-Only Membership Inference Attacks
by: Peng, Yuefeng, et al.
Published: (2024)
by: Peng, Yuefeng, et al.
Published: (2024)
Privacy Amplification for the Gaussian Mechanism via Bounded Support
by: Hu, Shengyuan, et al.
Published: (2024)
by: Hu, Shengyuan, et al.
Published: (2024)
Visual Privacy Auditing with Diffusion Models
by: Schwethelm, Kristian, et al.
Published: (2024)
by: Schwethelm, Kristian, et al.
Published: (2024)
Ensembler: Protect Collaborative Inference Privacy from Model Inversion Attack via Selective Ensemble
by: Liu, Dancheng, et al.
Published: (2024)
by: Liu, Dancheng, et al.
Published: (2024)
Hide in Plain Sight: Clean-Label Backdoor for Auditing Membership Inference
by: Chen, Depeng, et al.
Published: (2024)
by: Chen, Depeng, et al.
Published: (2024)
Label Inference Attacks against Node-level Vertical Federated GNNs
by: Arazzi, Marco, et al.
Published: (2023)
by: Arazzi, Marco, et al.
Published: (2023)
A Critical Review on the Effectiveness and Privacy Threats of Membership Inference Attacks
by: Jebreel, Najeeb, et al.
Published: (2026)
by: Jebreel, Najeeb, et al.
Published: (2026)
Sequentially Auditing Differential Privacy
by: González, Tomás, et al.
Published: (2025)
by: González, Tomás, et al.
Published: (2025)
Inference Privacy: Properties and Mechanisms
by: Tian, Fengwei, et al.
Published: (2024)
by: Tian, Fengwei, et al.
Published: (2024)
Membership Inference Attacks for Unseen Classes
by: Thaker, Pratiksha, et al.
Published: (2025)
by: Thaker, Pratiksha, et al.
Published: (2025)
Membership Inference Attacks and Privacy in Topic Modeling
by: Manzonelli, Nico, et al.
Published: (2024)
by: Manzonelli, Nico, et al.
Published: (2024)
Optimizing Canaries for Privacy Auditing with Metagradient Descent
by: Boglioni, Matteo, et al.
Published: (2025)
by: Boglioni, Matteo, et al.
Published: (2025)
Auditing $f$-Differential Privacy in One Run
by: Mahloujifar, Saeed, et al.
Published: (2024)
by: Mahloujifar, Saeed, et al.
Published: (2024)
Privacy-Preserving Mechanisms Enable Cheap Verifiable Inference of LLMs
by: Pal, Arka, et al.
Published: (2026)
by: Pal, Arka, et al.
Published: (2026)
FRIDA: Free-Rider Detection using Privacy Attacks
by: Recasens, Pol G., et al.
Published: (2024)
by: Recasens, Pol G., et al.
Published: (2024)
Auditing Differential Privacy Guarantees Using Density Estimation
by: Koskela, Antti, et al.
Published: (2024)
by: Koskela, Antti, et al.
Published: (2024)
Revisiting Label Inference Attacks in Vertical Federated Learning: Why They Are Vulnerable and How to Defend
by: Liu, Yige, et al.
Published: (2026)
by: Liu, Yige, et al.
Published: (2026)
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)
Rectifying Privacy and Efficacy Measurements in Machine Unlearning: A New Inference Attack Perspective
by: Naderloui, Nima, et al.
Published: (2025)
by: Naderloui, Nima, et al.
Published: (2025)
Differentially Private Optimization for Non-Decomposable Objective Functions
by: Kong, Weiwei, et al.
Published: (2023)
by: Kong, Weiwei, et al.
Published: (2023)
Similarity-based Label Inference Attack against Training and Inference of Split Learning
by: Liu, Junlin, et al.
Published: (2022)
by: Liu, Junlin, et al.
Published: (2022)
Auditing Differential Privacy in the Black-Box Setting
by: Shi, Kaining, et al.
Published: (2025)
by: Shi, Kaining, et al.
Published: (2025)
EC-LDA : Label Distribution Inference Attack against Federated Graph Learning with Embedding Compression
by: Cheng, Tong, et al.
Published: (2025)
by: Cheng, Tong, et al.
Published: (2025)
How Well Can Differential Privacy Be Audited in One Run?
by: Keinan, Amit, et al.
Published: (2025)
by: Keinan, Amit, et al.
Published: (2025)
PANORAMIA: Privacy Auditing of Machine Learning Models without Retraining
by: Kazmi, Mishaal, et al.
Published: (2024)
by: Kazmi, Mishaal, et al.
Published: (2024)
Metric Privacy in Federated Learning for Medical Imaging: Improving Convergence and Preventing Client Inference Attacks
by: Díaz, Judith Sáinz-Pardo, et al.
Published: (2025)
by: Díaz, Judith Sáinz-Pardo, et al.
Published: (2025)
Privacy Against Agnostic Inference Attacks in Vertical Federated Learning
by: Varasteh, Morteza
Published: (2023)
by: Varasteh, Morteza
Published: (2023)
Nearly Optimal Sample Complexity for Learning with Label Proportions
by: Busa-Fekete, Robert, et al.
Published: (2025)
by: Busa-Fekete, Robert, et al.
Published: (2025)
Similar Items
-
Privacy in Metalearning and Multitask Learning: Modeling and Separations
by: Aliakbarpour, Maryam, et al.
Published: (2024) -
The Sample Complexity of Membership Inference and Privacy Auditing
by: Haghifam, Mahdi, et al.
Published: (2025) -
A Unified Framework for Adversary-Aware Differential Privacy Bounds
by: Swanberg, Marika, et al.
Published: (2025) -
Observational Auditing of Label Privacy
by: Kalemaj, Iden, et al.
Published: (2025) -
ATTAXONOMY: Unpacking Differential Privacy Guarantees Against Practical Adversaries
by: Cummings, Rachel, et al.
Published: (2024)