RaMark: Radioactive Watermarking for Generated Tabular Data

arXiv:2607.09000 · cs.CR, cs.LG · Submitted 2026-07-10 · Read on arXiv

Xin Che, Lingyang Chu, Qiqi Zhang, Xinyu Ma, Xuan Luo, Jian Pei

cs.CR, cs.LG

Submitted: 2026-07-10

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• AUC — 65× in this paper · explained in 2 episodes
• Robustness — 30× in this paper · explained in 5 episodes
• Generative Model — 27× in this paper · explained in 2 episodes
• Diffusion Model — 24× in this paper · explained in 8 episodes
• Diffusion Models — 15× in this paper · explained in 6 episodes
• Verification — 10× in this paper · explained in 2 episodes
• Generative Models — 7× in this paper · explained in 2 episodes
• Generative Modeling — 4× in this paper · explained in 3 episodes
• Aggregation — 3× in this paper · explained in 1 episode
• Alignment — 3× in this paper · explained in 1 episode
• Diffusion Process — 3× in this paper · explained in 2 episodes
• Downstream Tasks — 3× in this paper · explained in 2 episodes
• Decode — 2× in this paper · explained in 2 episodes
• Fidelity — 2× in this paper · explained in 2 episodes
• Generative AI — 2× in this paper · explained in 2 episodes
• Higgs Small — 2× in this paper
• Synthetic Data Generation — 2× in this paper · explained in 5 episodes
• Benchmark — 1× in this paper · explained in 2 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Fingerprinting — 1× in this paper · explained in 2 episodes
• Generative Adversarial Networks (GANs) — 1× in this paper · explained in 3 episodes
• Latent Diffusion Model — 1× in this paper · explained in 2 episodes
• Latent Diffusion Models — 1× in this paper · explained in 2 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Machine Learning Efficiency — 1× in this paper
• Model-Agnostic — 1× in this paper · explained in 2 episodes
• RAG — 1× in this paper · explained in 3 episodes
• Selection Bias — 1× in this paper · explained in 4 episodes
• Trace — 1× in this paper · explained in 2 episodes

Sources

• Comprehensive Exploration of Synthetic Data Generation: A Survey
• Synthesizing Mixed-type Electronic Health Records using Diffusion Models
• TAG-WM: Tamper-Aware Generative Image Watermarking via Diffusion Inversion Sensitivity
• Systematic Assessment of Tabular Data Synthesis
• MUSE: Model-Agnostic Tabular Watermarking via Multi-Sample Selection
• Watermarking Generative Categorical Data
• Watermarking Generative Tabular Data
• A Comprehensive Survey of Watermarking Relational Databases Research
• Tabular Data Generation Models: An In-Depth Survey and Performance Benchmarks with Extensive Tuning
• A Comprehensive Survey on Generative Diffusion Models for Structured Data
• HMARK: Radioactive Multi-Bit Semantic-Latent Watermarking for Diffusion Models
• Adaptive and Robust Watermark for Generative Tabular Data
• Synthetic Data Applications in Finance
• TabDiff: a Mixed-type Diffusion Model for Tabular Data Generation
• A Comprehensive Survey of Synthetic Tabular Data Generation
• A Survey on Deep Learning Approaches for Tabular Data Generation: Utility, Alignment, Fidelity, Privacy, Diversity, and Beyond
• HARMONIC: Harnessing LLMs for Tabular Data Synthesis and Privacy Protection
• Mixed-Type Tabular Data Synthesis with Score-based Diffusion in Latent Space
• Provable Robust Watermarking for AI-Generated Text
• A Recipe for Watermarking Diffusion Models

Related papers

• SoK: AI-Augmented Binary Reversing
• Relaxed Sender Anonymity for CBDC Interbank Settlement: A Zero-Knowledge Approach on Permissioned EVM
• Calibration-Family Overfit: Why Trusted Sabotage Monitors Don't Transfer Across Lineages
• Efficient Fuzzy PSI under One-Sided Assumptions
• Sealing the Audit-Runtime Gap for LLM Skills
• Token Composition: A Graph Based on EVM Logs