Not All Entities are Created Equal: A Dynamic Anonymization Framework for Privacy-Preserving Retrieval-Augmented Generation

• URL: http://arxiv.org/abs/2603.26074v1
• Date: Fri, 27 Mar 2026 05:03:24 GMT
• Title: Not All Entities are Created Equal: A Dynamic Anonymization Framework for Privacy-Preserving Retrieval-Augmented Generation
• Abstract summary: We introduce a dynamic anonymization framework named TRIP-RAG.<n>It identifies highly sensitive entities while trading off utility.<n>Our theoretical analysis and experiments indicate that TRIP-RAG can effectively reduce context inference risks.
• Score: 7.905833478464377
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Retrieval-Augmented Generation (RAG) enhances the utility of Large Language Models (LLMs) by retrieving external documents. Since the knowledge databases in RAG are predominantly utilized via cloud services, private data in sensitive domains such as finance and healthcare faces the risk of personal information leakage. Thus, effectively anonymizing knowledge bases is crucial for privacy preservation. Existing studies equate the privacy risk of text to the linear superposition of the privacy risks of individual, isolated sensitive entities. The "one-size-fits-all" full processing of all sensitive entities severely degrades utility of LLM. To address this issue, we introduce a dynamic anonymization framework named TRIP-RAG. Based on context-aware entity quantification, this framework evaluates entities from the perspectives of marginal privacy risk, knowledge divergence, and topical relevance. It identifies highly sensitive entities while trading off utility, providing a feasible approach for variable-intensity privacy protection scenarios. Our theoretical analysis and experiments indicate that TRIP-RAG can effectively reduce context inference risks. Extensive experimental results demonstrate that, while maintaining privacy protection comparable to full anonymization, TRIP-RAG's Recall@k decreases by less than 35% compared to the original data, and the generation quality improves by up to 56% over existing baselines.

Related papers

• A Case Study on the Impact of Anonymization Along the RAG Pipeline [16.51235052027641]
  Using anonymization techniques to remove personally identifiable information (PII) and other sensitive markers in the underlying data represents a practical and sensible course of action for RAG administrators.<n>We systematically and empirically measure the impact of anonymization at two important points along the RAG pipeline: the dataset and generated answer.<n>We show that differences in privacy-utility trade-offs can be observed depending on where anonymization took place, demonstrating the significance of privacy risk mitigation placement in RAG.
  arXiv  Detail & Related papers  (2026-04-17T11:23:59Z)
• SoK: Privacy Risks and Mitigations in Retrieval-Augmented Generation Systems [53.51921540246166]
  Retrieval-Augmented Generation (RAG) techniques have become widely popular.<n>RAG involves the coupling of Large Language Models (LLMs) with domain-specific knowledge bases.<n>The proliferation of RAG has sparked concerns about data privacy.
  arXiv  Detail & Related papers  (2026-01-07T14:50:41Z)
• Zero-Shot Privacy-Aware Text Rewriting via Iterative Tree Search [60.197239728279534]
  Large language models (LLMs) in cloud-based services have raised significant privacy concerns.<n>Existing text anonymization and de-identification techniques, such as rule-based redaction and scrubbing, often struggle to balance privacy preservation with text naturalness and utility.<n>We propose a zero-shot, tree-search-based iterative sentence rewriting algorithm that systematically obfuscates or deletes private information while preserving coherence, relevance, and naturalness.
  arXiv  Detail & Related papers  (2025-09-25T07:23:52Z)
• SynBench: A Benchmark for Differentially Private Text Generation [35.908455649647784]
  Data-driven decision support in high-stakes domains like healthcare and finance faces significant barriers to data sharing.<n>Recent generative AI models, such as large language models, have shown impressive performance in open-domain tasks.<n>But their adoption in sensitive environments remains limited by unpredictable behaviors and insufficient privacy-preserving datasets.
  arXiv  Detail & Related papers  (2025-09-18T03:57:50Z)
• On the MIA Vulnerability Gap Between Private GANs and Diffusion Models [51.53790101362898]
  Generative Adversarial Networks (GANs) and diffusion models have emerged as leading approaches for high-quality image synthesis.<n>We present the first unified theoretical and empirical analysis of the privacy risks faced by differentially private generative models.
  arXiv  Detail & Related papers  (2025-09-03T14:18:22Z)
• RL-Finetuned LLMs for Privacy-Preserving Synthetic Rewriting [17.294176570269]
  We propose a reinforcement learning framework that fine-tunes a large language model (LLM) using a composite reward function.<n>The privacy reward combines semantic cues with structural patterns derived from a minimum spanning tree (MST) over latent representations.<n> Empirical results show that the proposed method significantly enhances author obfuscation and privacy metrics without degrading semantic quality.
  arXiv  Detail & Related papers  (2025-08-25T04:38:19Z)
• Privacy-Preserving Federated Embedding Learning for Localized Retrieval-Augmented Generation [60.81109086640437]
  We propose a novel framework called Federated Retrieval-Augmented Generation (FedE4RAG)<n>FedE4RAG facilitates collaborative training of client-side RAG retrieval models.<n>We apply homomorphic encryption within federated learning to safeguard model parameters.
  arXiv  Detail & Related papers  (2025-04-27T04:26:02Z)
• Privacy-Preserving Retrieval-Augmented Generation with Differential Privacy [25.896416088293908]
  retrieval-augmented generation (RAG) is particularly effective in assisting large language models (LLMs)<n>RAG outputs risk leaking sensitive information from the external data source.<n>We propose an algorithm that smartly spends privacy budget only for the tokens that require the sensitive information.
  arXiv  Detail & Related papers  (2024-12-06T01:20:16Z)
• Robust Utility-Preserving Text Anonymization Based on Large Language Models [80.5266278002083]
  Anonymizing text that contains sensitive information is crucial for a wide range of applications.<n>Existing techniques face the emerging challenges of the re-identification ability of large language models.<n>We propose a framework composed of three key components: a privacy evaluator, a utility evaluator, and an optimization component.
  arXiv  Detail & Related papers  (2024-07-16T14:28:56Z)
• How Do Input Attributes Impact the Privacy Loss in Differential Privacy? [55.492422758737575]
  We study the connection between the per-subject norm in DP neural networks and individual privacy loss. We introduce a novel metric termed the Privacy Loss-Input Susceptibility (PLIS) which allows one to apportion the subject's privacy loss to their input attributes.
  arXiv  Detail & Related papers  (2022-11-18T11:39:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.