Cover Image

Synthetic Tabular Data: Methods, Attacks and Defenses

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Cormode, Graham, Maddock, Samuel, Ullah, Enayat, Gade, Shripad
Format:	Preprint
Published:	2025
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

Leveraging Vertical Public-Private Split for Improved Synthetic Data Generation
by: Maddock, Samuel, et al.
Published: (2025)

FLAIM: AIM-based Synthetic Data Generation in the Federated Setting
by: Maddock, Samuel, et al.
Published: (2023)

GEM+: Scalable State-of-the-Art Private Synthetic Data with Generator Networks
by: Maddock, Samuel, et al.
Published: (2025)

Federated Computation of ROC and PR Curves
by: Xu, Xuefeng, et al.
Published: (2025)

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

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

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

Diffusion-Driven Synthetic Tabular Data Generation for Enhanced DoS/DDoS Attack Classification
by: B, Aravind, et al.
Published: (2026)

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

Data Reconstruction Attacks and Defenses: A Systematic Evaluation
by: Liu, Sheng, et al.
Published: (2024)

Constrained Adaptive Attack: Effective Adversarial Attack Against Deep Neural Networks for Tabular Data
by: Simonetto, Thibault, et al.
Published: (2024)

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

SecureLearn -- An Attack-agnostic Defense for Multiclass Machine Learning Against Data Poisoning Attacks
by: Paracha, Anum, et al.
Published: (2025)

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

Differentially Private Non-Convex Optimization under the KL Condition with Optimal Rates
by: Menart, Michael, et al.
Published: (2023)

Public-data Assisted Private Stochastic Optimization: Power and Limitations
by: Ullah, Enayat, et al.
Published: (2024)

Detecting Distributed Denial of Service Attacks Using Logistic Regression and SVM Methods
by: Ullah, Mohammad Arafat, et al.
Published: (2024)

Contrastive Learning-Based privacy metrics in Tabular Synthetic Datasets
by: Palacios, Milton Nicolás Plasencia, et al.
Published: (2025)

FedMID: A Data-Free Method for Using Intermediate Outputs as a Defense Mechanism Against Poisoning Attacks in Federated Learning
by: Han, Sungwon, et al.
Published: (2024)

Attack and Defense of Deep Learning Models in the Field of Web Attack Detection
by: Shi, Lijia, et al.
Published: (2024)

A Taxonomy of Attacks and Defenses in Split Learning
by: Shabbir, Aqsa, et al.
Published: (2025)

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

A Data-Driven Defense against Edge-case Model Poisoning Attacks on Federated Learning
by: Purohit, Kiran, et al.
Published: (2023)

Dashed Line Defense: Plug-And-Play Defense Against Adaptive Score-Based Query Attacks
by: Fu, Yanzhang, et al.
Published: (2026)

A No-Defense Defense Against Gradient-Based Adversarial Attacks on ML-NIDS: Is Less More?
by: elShehaby, Mohamed, et al.
Published: (2026)

Investigating Imperceptibility of Adversarial Attacks on Tabular Data: An Empirical Analysis
by: He, Zhipeng, et al.
Published: (2024)

Adaptive Attacks Break Defenses Against Indirect Prompt Injection Attacks on LLM Agents
by: Zhan, Qiusi, et al.
Published: (2025)

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

A Linear Reconstruction Approach for Attribute Inference Attacks against Synthetic Data
by: Annamalai, Meenatchi Sundaram Muthu Selva, et al.
Published: (2023)

Uncovering Attacks and Defenses in Secure Aggregation for Federated Deep Learning
by: Zhang, Yiwei, et al.
Published: (2024)

PECAN: A Deterministic Certified Defense Against Backdoor Attacks
by: Zhang, Yuhao, et al.
Published: (2023)

SyntheticPop: Attacking Speaker Verification Systems With Synthetic VoicePops
by: Jamdar, Eshaq, et al.
Published: (2025)

Open-Weight LLM Fine-Tuning Defenses are Susceptible to Simple Attacks
by: Kuo, Kevin, et al.
Published: (2026)

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

A Conditional Tabular GAN-Enhanced Intrusion Detection System for Rare Attacks in IoT Networks
by: Menssouri, Safaa, et al.
Published: (2025)

A Multi-Agent LLM Defense Pipeline Against Prompt Injection Attacks
by: Hossain, S M Asif, et al.
Published: (2025)

Inferring Sensitive Attributes from Knowledge Graph Embeddings: Attack and Defense Strategies
by: Hayder, Yasmine
Published: (2026)

Exploiting Defenses against GAN-Based Feature Inference Attacks in Federated Learning
by: Luo, Xinjian, et al.
Published: (2020)

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

Verifiable Differential Privacy
by: Biswas, Ari, et al.
Published: (2022)