Rethinking Anonymity Claims in Synthetic Data Generation: A Model-Centric Privacy Attack Perspective
Fuente:
arXiv
Saved in:
| Main Authors: | Ganev, Georgi, De Cristofaro, Emiliano |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
The Inadequacy of Similarity-based Privacy Metrics: Privacy Attacks against "Truly Anonymous" Synthetic Datasets
by: Ganev, Georgi, et al.
Published: (2023)
by: Ganev, Georgi, et al.
Published: (2023)
Understanding the Impact of Data Domain Extraction on Synthetic Data Privacy
by: Ganev, Georgi, et al.
Published: (2025)
by: Ganev, Georgi, et al.
Published: (2025)
Graphical vs. Deep Generative Models: Measuring the Impact of Differentially Private Mechanisms and Budgets on Utility
by: Ganev, Georgi, et al.
Published: (2023)
by: Ganev, Georgi, et al.
Published: (2023)
Synthetic Data, Similarity-based Privacy Metrics, and Regulatory (Non-)Compliance
by: Ganev, Georgi
Published: (2024)
by: Ganev, Georgi
Published: (2024)
The Importance of Being Discrete: Measuring the Impact of Discretization in End-to-End Differentially Private Synthetic Data
by: Ganev, Georgi, et al.
Published: (2025)
by: Ganev, Georgi, et al.
Published: (2025)
The Elusive Pursuit of Reproducing PATE-GAN: Benchmarking, Auditing, Debugging
by: Ganev, Georgi, et al.
Published: (2024)
by: Ganev, Georgi, et al.
Published: (2024)
Synthetic Data: Methods, Use Cases, and Risks
by: De Cristofaro, Emiliano
Published: (2023)
by: De Cristofaro, Emiliano
Published: (2023)
"What do you want from theory alone?" Experimenting with Tight Auditing of Differentially Private Synthetic Data Generation
by: Annamalai, Meenatchi Sundaram Muthu Selva, et al.
Published: (2024)
by: Annamalai, Meenatchi Sundaram Muthu Selva, et al.
Published: (2024)
The DCR Delusion: Measuring the Privacy Risk of Synthetic Data
by: Yao, Zexi, et al.
Published: (2025)
by: Yao, Zexi, et al.
Published: (2025)
A Systematic Review of Federated Generative Models
by: Gargary, Ashkan Vedadi, et al.
Published: (2024)
by: Gargary, Ashkan Vedadi, et al.
Published: (2024)
What's on Your Mind? Exploring Privacy of Mental Health Apps
by: Georgiou, Chloe, et al.
Published: (2026)
by: Georgiou, Chloe, et al.
Published: (2026)
SMOTE and Mirrors: Exposing Privacy Leakage from Synthetic Minority Oversampling
by: Ganev, Georgi, et al.
Published: (2025)
by: Ganev, Georgi, et al.
Published: (2025)
dpmm: Differentially Private Marginal Models, a Library for Synthetic Tabular Data Generation
by: Mahiou, Sofiane, et al.
Published: (2025)
by: Mahiou, Sofiane, et al.
Published: (2025)
Sharing is CAIRing: Characterizing Principles and Assessing Properties of Universal Privacy Evaluation for Synthetic Tabular Data
by: Hyrup, Tobias, et al.
Published: (2023)
by: Hyrup, Tobias, et al.
Published: (2023)
Synthetic Artifact Auditing: Tracing LLM-Generated Synthetic Data Usage in Downstream Applications
by: Wu, Yixin, et al.
Published: (2025)
by: Wu, Yixin, et al.
Published: (2025)
Tight Auditing of Differential Privacy in MST and AIM
by: Ganev, Georgi, et al.
Published: (2026)
by: Ganev, Georgi, et al.
Published: (2026)
A Survey of Privacy-Preserving Model Explanations: Privacy Risks, Attacks, and Countermeasures
by: Nguyen, Thanh Tam, et al.
Published: (2024)
by: Nguyen, Thanh Tam, et al.
Published: (2024)
Privacy-Preserving Data Linkage Across Private and Public Datasets for Collaborative Agriculture Research
by: Zafar, Osama, et al.
Published: (2024)
by: Zafar, Osama, et al.
Published: (2024)
LDPKiT: Superimposing Remote Queries for Privacy-Preserving Local Model Training
by: Li, Kexin, et al.
Published: (2024)
by: Li, Kexin, et al.
Published: (2024)
Nearly Tight Black-Box Auditing of Differentially Private Machine Learning
by: Annamalai, Meenatchi Sundaram Muthu Selva, et al.
Published: (2024)
by: Annamalai, Meenatchi Sundaram Muthu Selva, et al.
Published: (2024)
A Survey on Differential Privacy for SpatioTemporal Data in Transportation Research
by: Bhadani, Rahul
Published: (2024)
by: Bhadani, Rahul
Published: (2024)
Privacy-Preserving Dataset Combination
by: Fuentes, Keren, et al.
Published: (2025)
by: Fuentes, Keren, et al.
Published: (2025)
Position Paper: Assessing Robustness, Privacy, and Fairness in Federated Learning Integrated with Foundation Models
by: Wang, Jiaqi, et al.
Published: (2024)
by: Wang, Jiaqi, et al.
Published: (2024)
Personalized Differential Privacy for Ridge Regression
by: Acharya, Krishna, et al.
Published: (2024)
by: Acharya, Krishna, et al.
Published: (2024)
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)
FairDP: Certified Fairness with Differential Privacy
by: Tran, Khang, et al.
Published: (2023)
by: Tran, Khang, et al.
Published: (2023)
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)
De-amplifying Bias from Differential Privacy in Language Model Fine-tuning
by: Srivastava, Sanjari, et al.
Published: (2024)
by: Srivastava, Sanjari, et al.
Published: (2024)
Machine Unlearning Fails to Remove Data Poisoning Attacks
by: Pawelczyk, Martin, et al.
Published: (2024)
by: Pawelczyk, Martin, et al.
Published: (2024)
Privacy Vulnerabilities in Marginals-based Synthetic Data
by: Golob, Steven, et al.
Published: (2024)
by: Golob, Steven, et al.
Published: (2024)
Minerva: A File-Based Ransomware Detector
by: Hitaj, Dorjan, et al.
Published: (2023)
by: Hitaj, Dorjan, et al.
Published: (2023)
Blameless Users in a Clean Room: Defining Copyright Protection for Generative Models
by: Cohen, Aloni
Published: (2025)
by: Cohen, Aloni
Published: (2025)
Unifying Re-Identification, Attribute Inference, and Data Reconstruction Risks in Differential Privacy
by: Kulynych, Bogdan, et al.
Published: (2025)
by: Kulynych, Bogdan, et al.
Published: (2025)
Expected Harm: Rethinking Safety Evaluation of (Mis)Aligned LLMs
by: Chen, Yen-Shan, et al.
Published: (2026)
by: Chen, Yen-Shan, et al.
Published: (2026)
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)
To Shuffle or not to Shuffle: Auditing DP-SGD with Shuffling
by: Annamalai, Meenatchi Sundaram Muthu Selva, et al.
Published: (2024)
by: Annamalai, Meenatchi Sundaram Muthu Selva, et al.
Published: (2024)
Rethinking Graph Backdoor Attacks: A Distribution-Preserving Perspective
by: Zhang, Zhiwei, et al.
Published: (2024)
by: Zhang, Zhiwei, et al.
Published: (2024)
Rethinking Data Protection in the (Generative) Artificial Intelligence Era
by: Li, Yiming, et al.
Published: (2025)
by: Li, Yiming, et al.
Published: (2025)
Scalable and Privacy-Preserving Synthetic Data Generation on Decentralised Web
by: Ramesh, Vishal, et al.
Published: (2023)
by: Ramesh, Vishal, et al.
Published: (2023)
Why Data Anonymization Has Not Taken Off
by: Schneider, Matthew J., et al.
Published: (2025)
by: Schneider, Matthew J., et al.
Published: (2025)
Similar Items
-
The Inadequacy of Similarity-based Privacy Metrics: Privacy Attacks against "Truly Anonymous" Synthetic Datasets
by: Ganev, Georgi, et al.
Published: (2023) -
Understanding the Impact of Data Domain Extraction on Synthetic Data Privacy
by: Ganev, Georgi, et al.
Published: (2025) -
Graphical vs. Deep Generative Models: Measuring the Impact of Differentially Private Mechanisms and Budgets on Utility
by: Ganev, Georgi, et al.
Published: (2023) -
Synthetic Data, Similarity-based Privacy Metrics, and Regulatory (Non-)Compliance
by: Ganev, Georgi
Published: (2024) -
The Importance of Being Discrete: Measuring the Impact of Discretization in End-to-End Differentially Private Synthetic Data
by: Ganev, Georgi, et al.
Published: (2025)