✨Benchmarking Knowledge-Extraction Attack and Defense on Retrieval-Augmented Generation
📝 Summary:
This paper introduces the first systematic benchmark for evaluating knowledge-extraction attacks and defenses on Retrieval-Augmented Generation systems. It standardizes testing across diverse models and strategies to enable comparable evaluation and help build privacy-preserving RAG.
🔹 Publication Date: Published on Feb 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.09319
• PDF: https://arxiv.org/pdf/2602.09319
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#RAG #KnowledgeExtraction #Cybersecurity #AIPrivacy #Benchmarking
📝 Summary:
This paper introduces the first systematic benchmark for evaluating knowledge-extraction attacks and defenses on Retrieval-Augmented Generation systems. It standardizes testing across diverse models and strategies to enable comparable evaluation and help build privacy-preserving RAG.
🔹 Publication Date: Published on Feb 10
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2602.09319
• PDF: https://arxiv.org/pdf/2602.09319
==================================
For more data science resources:
✓ https://t.iss.one/DataScienceT
#RAG #KnowledgeExtraction #Cybersecurity #AIPrivacy #Benchmarking