arXiv (All)
open access
On Privacy in Data-Space Tabular Diffusion Models: Influential Factors, Attacker Knowledge, and Metrics
Tabular data plays an important role in many fields and industries, including those with elevated privacy considerations and risks. As such, there is a rising interest in generating high-quality synthetic proxies for real tabular data as a means of reducing privacy risk and proprietary data exposure
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- PCDM: A Diffusion-Based Data Poisoning Attack Against Federated Learning Systems#194283
- An Empirical Study of Utility and Disclosure Risk for Tabular Data Synthesis Models: In-Depth Analysis and Interesting Findings#405976
- Privacy in Statistical Databases : International Conference, PSD 2026, Cádiz, Spain, September 30–October 2, 2026, Proceedings / edited by Josep Domingo-Ferrer, Ismael González-Yero#970189
- Gustavosta/Stable-Diffusion-Prompts#786560
- Privacy Vulnerabilities of Attention Layers in Tabular Foundation Models and Protection of High-Risk Queries#306946
Record · ID 1115541
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive