# A Survey on Current Trends and Recent Advances in Text Anonymization

arXiv: https://arxiv.org/abs/2508.21587 · HTML: https://mlsanity.com/paper/2508.21587/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 83 references that are in the map.
- First posted on arXiv 29 Aug 2025.

## Builds on (most cited first, 30 of 36)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [GPT-4o System Card](https://mlsanity.com/paper/2410.21276/) (2024)
- [DeID-GPT: Zero-shot Medical Text De-Identification by GPT-4](https://mlsanity.com/paper/2303.11032/) (2023)
- [The Text Anonymization Benchmark (TAB): A Dedicated Corpus and Evaluation Framework for Text Anonymization](https://mlsanity.com/paper/2202.00443/) (2022)
- [DP-Rewrite: Towards Reproducibility and Transparency in Differentially Private Text Rewriting](https://mlsanity.com/paper/2208.10400/) (2022)
- [DP-BART for Privatized Text Rewriting under Local Differential Privacy](https://mlsanity.com/paper/2302.07636/) (2023)
- [DP-MLM: Differentially Private Text Rewriting Using Masked Language Models](https://mlsanity.com/paper/2407.00637/) (2024)
- [IncogniText: Privacy-enhancing Conditional Text Anonymization via LLM-based Private Attribute Randomization](https://mlsanity.com/paper/2407.02956/) (2024)
- [Privacy enabled Financial Text Classification using Differential Privacy and Federated Learning](https://mlsanity.com/paper/2110.01643/) (2021)
- [Privacy- and Utility-Preserving NLP with Anonymized Data: A case study of Pseudonymization](https://mlsanity.com/paper/2306.05561/) (2023)
- [De-identification is not enough: a comparison between de-identified and synthetic clinical notes](https://mlsanity.com/paper/2402.00179/) (2024)
- [No Intruder, no Validity: Evaluation Criteria for Privacy-Preserving Text Anonymization](https://mlsanity.com/paper/2103.09263/) (2021)
- [De-identification of clinical free text using natural language processing: A systematic review of current approaches](https://mlsanity.com/paper/2312.03736/) (2023)
- [Textwash -- automated open-source text anonymisation](https://mlsanity.com/paper/2208.13081/) (2022)
- [Robust Utility-Preserving Text Anonymization Based on Large Language Models](https://mlsanity.com/paper/2407.11770/) (2024)
- [Privacy-Preserving Synthetic Educational Data Generation](https://mlsanity.com/paper/2207.03202/) (2022)
- [Bootstrapping Text Anonymization Models with Distant Supervision](https://mlsanity.com/paper/2205.06895/) (2022)
- [Truthful Text Sanitization Guided by Inference Attacks](https://mlsanity.com/paper/2412.12928/) (2024)
- [Anonymity at Risk? Assessing Re-Identification Capabilities of Large Language Models](https://mlsanity.com/paper/2308.11103/) (2023)
- [JAMDEC: Unsupervised Authorship Obfuscation using Constrained Decoding over Small Language Models](https://mlsanity.com/paper/2402.08761/) (2024)
- [Unlocking the Potential of Large Language Models for Clinical Text Anonymization: A Comparative Study](https://mlsanity.com/paper/2406.00062/) (2024)
- [Informed Named Entity Recognition Decoding for Generative Language Models](https://mlsanity.com/paper/2308.07791/) (2023)
- [LAW: Legal Agentic Workflows for Custody and Fund Services Contracts](https://mlsanity.com/paper/2412.11063/) (2024)
- [May I Ask Who's Calling? Named Entity Recognition on Call Center Transcripts for Privacy Law Compliance](https://mlsanity.com/paper/2010.15598/) (2020)
- [A Comparative Evaluation Of Transformer Models For De-Identification Of Clinical Text Data](https://mlsanity.com/paper/2204.07056/) (2022)
- [Benchmarking Advanced Text Anonymisation Methods: A Comparative Study on Novel and Traditional Approaches](https://mlsanity.com/paper/2404.14465/) (2024)
- [TAROT: Task-Oriented Authorship Obfuscation Using Policy Optimization Methods](https://mlsanity.com/paper/2407.21630/) (2024)
- [Advancing privacy in learning analytics using differential privacy](https://mlsanity.com/paper/2501.01786/) (2025)
- [Named Entity Recognition for Audio De-Identification](https://mlsanity.com/paper/2204.12622/) (2022)

## Built on by (most cited first, 5 of 5)

- [Adaptive Text Anonymization: Learning Privacy-Utility Trade-offs via Prompt Optimization](https://mlsanity.com/paper/2602.20743/) (2026)
- [Protecting De-identified Documents from Search-based Linkage Attacks](https://mlsanity.com/paper/2510.06383/) (2025)
- [Introducing the Privacy-HSD Trade-off: Hate Speech Detection, but not at the Cost of Privacy](https://mlsanity.com/paper/2608.19006/) (2026)
- [PrivBench: A Holistic and Modular Benchmarking Platform for Evaluating Text-to-Text Privatization](https://mlsanity.com/paper/2608.29624/) (2026)
- [On the Impact of Anonymization on the Performance of Large Language Models](https://mlsanity.com/paper/2609.11335/) (2026)

## Abstract (arXiv)

The proliferation of textual data containing sensitive personal information across various domains requires robust anonymization techniques to protect privacy and comply with regulations, while preserving data usability for diverse and crucial downstream tasks. This survey provides a comprehensive overview of current trends and recent advances in text anonymization techniques. We begin by discussing foundational approaches, primarily centered on Named Entity Recognition, before examining the transformative impact of Large Language Models, detailing their dual role as sophisticated anonymizers and potent de-anonymization threats. The survey further explores domain-specific challenges and tailored solutions in critical sectors such as healthcare, law, finance, and education. We investigate advanced methodologies incorporating formal privacy models and risk-aware frameworks, and address the specialized subfield of authorship anonymization. Additionally, we review evaluation frameworks, comprehensive metrics, benchmarks, and practical toolkits for real-world deployment of anonymization solutions. This review consolidates current knowledge, identifies emerging trends and persistent challenges, including the evolving privacy-utility trade-off, the need to address quasi-identifiers, and the implications of LLM capabilities, and aims to guide future research directions for both academics and practitioners in this field.