LLM-PBE: Assessing Data Privacy in Large Language Models
Summary: LLM-PBE: toolkit for systematic evaluation of training-data privacy leakage in LLMs across the model lifecycle, unifying diverse attacks, defenses, data modalities, and privacy metrics. Experiments show model scale, data properties, and temporal drift shape leakage; artifacts and benchmarks released for reproducible privacy research. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Qinbin Li
- 2. Junyuan Hong
- 3. Chulin Xie
- 4. Jeffrey Tan
- 5. Rachel Xin
- 6. Junyi Hou
- 7. Xavier Yin
- 8. Zhun Wang
- 9. Dan Hendrycks
- 10. Zhangyang Wang
- 11. Bo Li
- 12. Bingsheng He
- 13. Dawn Song
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Outgoing Citations (Sorted by Pagerank)
Showing 6 of 6 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 40 | Privacy-Preserving Data Mining | 2000 | SIGMOD | 0.00074232718 |
| 654 | Anatomy: Simple and Effective Privacy Preservation | 2006 | VLDB | 0.00018613167 |
| 984 | Natural language to SQL: Where are we today? | 2020 | VLDB | 0.00014857465 |
| 1,732 | CatSQL: Towards Real World Natural Language to SQL Applications | 2023 | VLDB | 0.00010732004 |
| 3,995 | How Large Language Models Will Disrupt Data Management | 2023 | VLDB | 6.5513237e-05 |
| 4,934 | From BERT to GPT-3 Codex: Harnessing the Potential of Very Large Language Models for Data Management | 2022 | VLDB | 5.8198826e-05 |
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