Cover Image

Data Plagiarism Index: Characterizing the Privacy Risk of Data-Copying in Tabular Generative Models

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Ward, Joshua, Wang, Chi-Hua, Cheng, Guang
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

Synth-MIA: A Testbed for Auditing Privacy Leakage in Tabular Data Synthesis
by: Ward, Joshua, et al.
Published: (2025)

Ensembling Membership Inference Attacks Against Tabular Generative Models
by: Ward, Joshua, et al.
Published: (2025)

Watermarking Generative Tabular Data
by: He, Hengzhi, et al.
Published: (2024)

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

BadGD: A unified data-centric framework to identify gradient descent vulnerabilities
by: Wang, Chi-Hua, et al.
Published: (2024)

Do You Really Need Public Data? Surrogate Public Data for Differential Privacy on Tabular Data
by: Hod, Shlomi, et al.
Published: (2025)

Watermarking Generative Categorical Data
by: Gu, Bochao, et al.
Published: (2024)

Sharing is CAIRing: Characterizing Principles and Assessing Properties of Universal Privacy Evaluation for Synthetic Tabular Data
by: Hyrup, Tobias, et al.
Published: (2023)

Initial Exploration of Zero-Shot Privacy Utility Tradeoffs in Tabular Data Using GPT-4
by: Mandal, Bishwas, et al.
Published: (2024)

Where have you been? A Study of Privacy Risk for Point-of-Interest Recommendation
by: Cai, Kunlin, et al.
Published: (2023)

Is API Access to LLMs Useful for Generating Private Synthetic Tabular Data?
by: Swanberg, Marika, et al.
Published: (2025)

Generated Data with Fake Privacy: Hidden Dangers of Fine-tuning Large Language Models on Generated Data
by: Akkus, Atilla, et al.
Published: (2024)

Learning to Attack: Uncovering Privacy Risks in Sequential Data Releases
by: Cui, Ziyao, et al.
Published: (2025)

Beyond Data Privacy: New Privacy Risks for Large Language Models
by: Du, Yuntao, et al.
Published: (2025)

Synthetic Tabular Data: Methods, Attacks and Defenses
by: Cormode, Graham, et al.
Published: (2025)

Robust Spectral Watermark for Synthetic Tabular Data
by: Zhao, Yizhou, et al.
Published: (2025)

Explaining the Model, Protecting Your Data: Revealing and Mitigating the Data Privacy Risks of Post-Hoc Model Explanations via Membership Inference
by: Huang, Catherine, et al.
Published: (2024)

Privacy Re-identification Attacks on Tabular GANs
by: Alshantti, Abdallah, et al.
Published: (2024)

HARMONIC: Harnessing LLMs for Tabular Data Synthesis and Privacy Protection
by: Wang, Yuxin, et al.
Published: (2024)

Backdoor Attacks on Transformers for Tabular Data: An Empirical Study
by: Pleiter, Bart, et al.
Published: (2023)

Systematic Assessment of Tabular Data Synthesis
by: Du, Yuntao, et al.
Published: (2024)

DMRL: Data- and Model-aware Reward Learning for Data Extraction
by: Wang, Zhiqiang, et al.
Published: (2025)

SafeSynthDP: Leveraging Large Language Models for Privacy-Preserving Synthetic Data Generation Using Differential Privacy
by: Nahid, Md Mahadi Hasan, et al.
Published: (2024)

Privacy-Preserving Statistical Data Generation: Application to Sepsis Detection
by: Macias-Fassio, Eric, et al.
Published: (2024)

Scalable and Privacy-Preserving Synthetic Data Generation on Decentralised Web
by: Ramesh, Vishal, et al.
Published: (2023)

Shake to Leak: Fine-tuning Diffusion Models Can Amplify the Generative Privacy Risk
by: Li, Zhangheng, et al.
Published: (2024)

Privacy Backdoors: Stealing Data with Corrupted Pretrained Models
by: Feng, Shanglun, et al.
Published: (2024)

Leveraging Randomness in Model and Data Partitioning for Privacy Amplification
by: Dong, Andy, et al.
Published: (2025)

Diverse Community Data for Benchmarking Data Privacy Algorithms
by: Sen, Aniruddha, et al.
Published: (2023)

Membership Inference over Diffusion-models-based Synthetic Tabular Data
by: Cheng, Peini, et al.
Published: (2025)

Beyond One-Size-Fits-All: Neural Networks for Differentially Private Tabular Data Synthesis
by: Chen, Kai, et al.
Published: (2025)

Understanding the Impact of Data Domain Extraction on Synthetic Data Privacy
by: Ganev, Georgi, et al.
Published: (2025)

Data Privacy Preservation on the Internet of Things
by: Sen, Jaydip, et al.
Published: (2023)

Managing Correlations in Data and Privacy Demand
by: Chaudhuri, Syomantak, et al.
Published: (2025)

Synthetic Data: Revisiting the Privacy-Utility Trade-off
by: Sarmin, Fatima Jahan, et al.
Published: (2024)

dpmm: Differentially Private Marginal Models, a Library for Synthetic Tabular Data Generation
by: Mahiou, Sofiane, et al.
Published: (2025)

The DCR Delusion: Measuring the Privacy Risk of Synthetic Data
by: Yao, Zexi, et al.
Published: (2025)

CatBack: Universal Backdoor Attacks on Tabular Data via Categorical Encoding
by: Tajalli, Behrad, et al.
Published: (2025)

Privacy Vulnerabilities in Marginals-based Synthetic Data
by: Golob, Steven, et al.
Published: (2024)

Training Data Reconstruction: Privacy due to Uncertainty?
by: Runkel, Christina, et al.
Published: (2024)