Anonymization placement in RAG—at the dataset or at the generated answer—creates observable differences in privacy protection versus response utility.
arXiv preprint arXiv:2409.08045 , year=
5 Pith papers cite this work, alongside 3 external citations. Polarity classification is still indexing.
5
Pith papers citing it
3
external citations · external index
citation-role summary
background 1
citation-polarity summary
years
2026 5verdicts
UNVERDICTED 5roles
background 1polarities
background 1representative citing papers
ADAM extracts data from LLM agent memory with up to 100% attack success rate by estimating data distribution and selecting queries via entropy guidance.
A multi-agent semantic rewriting system for RAG cuts targeted privacy leakage from 144 to 1 instances on LLaMA-3-8B while raising BLEU-1 to 0.122 over SAGE's 0.117, with offline preprocessing.
CanaryRAG detects RAG extraction attacks in real time by embedding canary tokens and checking dual target-oracle paths for integrity violations, achieving lower chunk recovery rates with negligible overhead.
ALDEN boosts private data extraction rates from RAG systems by combining active learning for query diversification with dynamic estimation of the underlying knowledge-base topic distribution.
citing papers explorer
-
A Case Study on the Impact of Anonymization Along the RAG Pipeline
Anonymization placement in RAG—at the dataset or at the generated answer—creates observable differences in privacy protection versus response utility.
-
ADAM: A Systematic Data Extraction Attack on Agent Memory via Adaptive Querying
ADAM extracts data from LLM agent memory with up to 100% attack success rate by estimating data distribution and selecting queries via entropy guidance.
-
Privacy-Preserving RAG via Multi-Agent Semantic Rewriting: Achieving Confidentiality Without Compromising Contextual Fidelity
A multi-agent semantic rewriting system for RAG cuts targeted privacy leakage from 144 to 1 instances on LLaMA-3-8B while raising BLEU-1 to 0.122 over SAGE's 0.117, with offline preprocessing.
-
Detecting RAG Extraction Attack via Dual-Path Runtime Integrity Game
CanaryRAG detects RAG extraction attacks in real time by embedding canary tokens and checking dual target-oracle paths for integrity violations, achieving lower chunk recovery rates with negligible overhead.
-
ALDEN: Boosting Private Data Extraction from Retrieval-Augmented Generation Systems via Active Learning and Distribution Estimation
ALDEN boosts private data extraction rates from RAG systems by combining active learning for query diversification with dynamic estimation of the underlying knowledge-base topic distribution.