# C-sanitized: a privacy model for document redaction and sanitization

arXiv: https://arxiv.org/abs/1406.4285 · HTML: https://mlsanity.com/paper/1406.4285/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jun 2014.

## Built on by (most cited first, 27 of 27)

- [The Text Anonymization Benchmark (TAB): A Dedicated Corpus and Evaluation Framework for Text Anonymization](https://mlsanity.com/paper/2202.00443/) (2022)
- [Digital Forgetting in Large Language Models: A Survey of Unlearning Methods](https://mlsanity.com/paper/2404.02062/) (2024)
- [Knowledge Sanitization of Large Language Models](https://mlsanity.com/paper/2309.11852/) (2023)
- [Large Language Models are Advanced Anonymizers](https://mlsanity.com/paper/2402.13846/) (2024)
- [Differentially Private Natural Language Models: Recent Advances and Future Directions](https://mlsanity.com/paper/2301.09112/) (2023)
- [Privacy- and Utility-Preserving NLP with Anonymized Data: A case study of Pseudonymization](https://mlsanity.com/paper/2306.05561/) (2023)
- [On a Utilitarian Approach to Privacy Preserving Text Generation](https://mlsanity.com/paper/2104.11838/) (2021)
- [Guiding Text-to-Text Privatization by Syntax](https://mlsanity.com/paper/2306.01471/) (2023)
- [Robust Utility-Preserving Text Anonymization Based on Large Language Models](https://mlsanity.com/paper/2407.11770/) (2024)
- [Bootstrapping Text Anonymization Models with Distant Supervision](https://mlsanity.com/paper/2205.06895/) (2022)
- [Enhancing the De-identification of Personally Identifiable Information in Educational Data](https://mlsanity.com/paper/2501.09765/) (2025)
- [Neural Text Sanitization with Privacy Risk Indicators: An Empirical Analysis](https://mlsanity.com/paper/2310.14312/) (2023)
- [Can pre-trained Transformers be used in detecting complex sensitive sentences? -- A Monsanto case study](https://mlsanity.com/paper/2203.06793/) (2022)
- [Is Your Model Sensitive? SPeDaC: A New Benchmark for Detecting and Classifying Sensitive Personal Data](https://mlsanity.com/paper/2208.06216/) (2022)
- [Cloaked Classifiers: Pseudonymization Strategies on Sensitive Classification Tasks](https://mlsanity.com/paper/2406.17875/) (2024)
- [Re-identification of De-identified Documents with Autoregressive Infilling](https://mlsanity.com/paper/2505.12859/) (2025)
- [Deception for Cyber Defence: Challenges and Opportunities](https://mlsanity.com/paper/2208.07127/) (2022)
- [PrIeD-KIE: Towards Privacy Preserved Document Key Information Extraction](https://mlsanity.com/paper/2310.03777/) (2023)
- [DP2Unlearning: An Efficient and Guaranteed Unlearning Framework for LLMs](https://mlsanity.com/paper/2504.13774/) (2025)
- [Silencing the Risk, Not the Whistle: A Semi-automated Text Sanitization Tool for Mitigating the Risk of Whistleblower Re-Identification](https://mlsanity.com/paper/2405.01097/) (2024)
- [AspirinSum: an Aspect-based utility-preserved de-identification Summarization framework](https://mlsanity.com/paper/2406.13947/) (2024)
- [Cross-Domain Transfer and Few-Shot Learning for Personal Identifiable Information Recognition](https://mlsanity.com/paper/2507.11862/) (2025)
- [Protecting De-identified Documents from Search-based Linkage Attacks](https://mlsanity.com/paper/2510.06383/) (2025)
- [Stronger Re-identification Attacks through Reasoning and Aggregation](https://mlsanity.com/paper/2510.09184/) (2025)
- [Sensitivity-Aware Retrieval-Augmented Intent Clarification](https://mlsanity.com/paper/2603.06025/) (2026)
- [The Conundrum of Trustworthy Research on Attacking Personally Identifiable Information Removal Techniques](https://mlsanity.com/paper/2603.08207/) (2026)
- [PrivBench: A Holistic and Modular Benchmarking Platform for Evaluating Text-to-Text Privatization](https://mlsanity.com/paper/2608.29624/) (2026)

## Abstract (arXiv)

Within the current context of Information Societies, large amounts of information are daily exchanged and/or released. The sensitive nature of much of this information causes a serious privacy threat when documents are uncontrollably made available to untrusted third parties. In such cases, appropriate data protection measures should be undertaken by the responsible organization, especially under the umbrella of current legislations on data privacy. To do so, human experts are usually requested to redact or sanitize document contents. To relieve this burdensome task, this paper presents a privacy model for document redaction/sanitization, which offers several advantages over other models available in the literature. Based on the well-established foundations of data semantics and the information theory, our model provides a framework to develop and implement automated and inherently semantic redaction/sanitization tools. Moreover, contrary to ad-hoc redaction methods, our proposal provides a priori privacy guarantees which can be intuitively defined according to current legislations on data privacy. Empirical tests performed within the context of several use cases illustrate the applicability of our model and its ability to mimic the reasoning of human sanitizers.