# Text Sanitization Beyond Specific Domains: Zero-Shot Redaction & Substitution with Large Language Models

arXiv: https://arxiv.org/abs/2311.10785 · HTML: https://mlsanity.com/paper/2311.10785/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 37 references that are in the map.
- First posted on arXiv 16 Nov 2023.

## Builds on (most cited first, 6 of 6)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [The Text Anonymization Benchmark (TAB): A Dedicated Corpus and Evaluation Framework for Text Anonymization](https://mlsanity.com/paper/2202.00443/) (2022)
- [Action-Based Conversations Dataset: A Corpus for Building More In-Depth Task-Oriented Dialogue Systems](https://mlsanity.com/paper/2104.00783/) (2021)
- [Behind the Mask: Demographic bias in name detection for PII masking](https://mlsanity.com/paper/2205.04505/) (2022)

## Built on by (most cited first, 10 of 10)

- [AutoAttacker: A Large Language Model Guided System to Implement Automatic Cyber-attacks](https://mlsanity.com/paper/2403.01038/) (2024)
- [IncogniText: Privacy-enhancing Conditional Text Anonymization via LLM-based Private Attribute Randomization](https://mlsanity.com/paper/2407.02956/) (2024)
- [Robust Utility-Preserving Text Anonymization Based on Large Language Models](https://mlsanity.com/paper/2407.11770/) (2024)
- [Towards Efficient Multi-LLM Inference: Characterization and Analysis of LLM Routing and Hierarchical Techniques](https://mlsanity.com/paper/2506.06579/) (2025)
- [Public Data Assisted Differentially Private In-Context Learning](https://mlsanity.com/paper/2509.10932/) (2025)
- [Anonymous-by-Construction: An LLM-Driven Framework for Privacy-Preserving Text](https://mlsanity.com/paper/2603.17217/) (2026)
- [RedacBench: Can AI Erase Your Secrets?](https://mlsanity.com/paper/2603.20208/) (2026)
- [IDT: Dual-Task Adversarial Attacks for Privacy Protection](https://mlsanity.com/paper/2406.19642/) (2024)
- [Image deidentification in the XNAT ecosystem: use cases and solutions](https://mlsanity.com/paper/2504.20657/) (2025)
- [AI-Driven Document Redaction in UK Public Authorities: Implementation Gaps, Regulatory Challenges, and the Human Oversight Imperative](https://mlsanity.com/paper/2512.02774/) (2025)

## Abstract (arXiv)

In the context of information systems, text sanitization techniques are used to identify and remove sensitive data to comply with security and regulatory requirements. Even though many methods for privacy preservation have been proposed, most of them are focused on the detection of entities from specific domains (e.g., credit card numbers, social security numbers), lacking generality and requiring customization for each desirable domain. Moreover, removing words is, in general, a drastic measure, as it can degrade text coherence and contextual information. Less severe measures include substituting a word for a safe alternative, yet it can be challenging to automatically find meaningful substitutions. We present a zero-shot text sanitization technique that detects and substitutes potentially sensitive information using Large Language Models. Our evaluation shows that our method excels at protecting privacy while maintaining text coherence and contextual information, preserving data utility for downstream tasks.