arXiv (All)
open access
Dataset Inference for Data Provenance and Privacy Auditing in Tabular Foundation Models
Tabular foundation models (TFMs) are increasingly deployed through black-box APIs and trained on large collections of real-world tabular datasets. As this training paradigm becomes more widely adopted, private or proprietary datasets may be incorporated into pre-training corpora, yet there are curre
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
- Is TabPFN the Silver Bullet for Insurance Pricing?#1297143
- Transparent Accountable Data Mining: New Strategies for Privacy Protection#400254
- Attestream: Usage-Aware Intermittent Data Distribution with Verifiable Lifecycle Provenance for Machine-Learning Data Streams#984550
- LLM-CEG: Extending the Classification Error Gauge Framework for Privacy Auditing of Large Language Models#138851
- TU/e FAIR Data Clinic Toolkit#1348467
Record · ID 1388844
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