# A Review of Anonymization for Healthcare Data

arXiv: https://arxiv.org/abs/2104.06523 · HTML: https://mlsanity.com/paper/2104.06523/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 131 references that are in the map.
- First posted on arXiv 13 Apr 2021.

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

- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [A generic framework for privacy preserving deep learning](https://mlsanity.com/paper/1811.04017/) (2018)
- [Membership Inference Attack on Graph Neural Networks](https://mlsanity.com/paper/2101.06570/) (2021)
- [Linear Program Reconstruction in Practice](https://mlsanity.com/paper/1810.05692/) (2018)
- [Individual Differential Privacy: A Utility-Preserving Formulation of Differential Privacy Guarantees](https://mlsanity.com/paper/1612.02298/) (2016)
- [t-Closeness through Microaggregation: Strict Privacy with Enhanced Utility Preservation](https://mlsanity.com/paper/1512.02909/) (2015)

## Built on by (most cited first, 12 of 12)

- [A Survey on Medical Document Summarization](https://mlsanity.com/paper/2212.01669/) (2022)
- [PIIvot: A Lightweight NLP Anonymization Framework for Question-Anchored Tutoring Dialogues](https://mlsanity.com/paper/2505.16931/) (2025)
- [Does Black-box Attribute Inference Attacks on Graph Neural Networks Constitute Privacy Risk?](https://mlsanity.com/paper/2306.00578/) (2023)
- [Protect and Extend -- Using GANs for Synthetic Data Generation of Time-Series Medical Records](https://mlsanity.com/paper/2402.14042/) (2024)
- [From Principle to Practice: Vertical Data Minimization for Machine Learning](https://mlsanity.com/paper/2311.10500/) (2023)
- [SoK: Privacy-aware LLM in Healthcare: Threat Model, Privacy Techniques, Challenges and Recommendations](https://mlsanity.com/paper/2601.10004/) (2026)
- [MLHOps: Machine Learning for Healthcare Operations](https://mlsanity.com/paper/2305.02474/) (2023)
- [Beyond the Generative Learning Trilemma: Generative Model Assessment in Data Scarcity Domains](https://mlsanity.com/paper/2504.10555/) (2025)
- [Federated Learning on Patient Data for Privacy-Protecting Polycystic Ovary Syndrome Treatment](https://mlsanity.com/paper/2308.11220/) (2023)
- [Statistical properties and privacy guarantees of an original distance-based fully synthetic data generation method](https://mlsanity.com/paper/2310.06571/) (2023)
- [Efficient and Privacy-Preserved Link Prediction via Condensed Graphs](https://mlsanity.com/paper/2503.12156/) (2025)
- [A Pipeline for Generating Longitudinal Synthetic Clinical Notes Using Large Language Models](https://mlsanity.com/paper/2606.26879/) (2026)

## Abstract (arXiv)

Mining health data can lead to faster medical decisions, improvement in the quality of treatment, disease prevention, reduced cost, and it drives innovative solutions within the healthcare sector. However, health data is highly sensitive and subject to regulations such as the General Data Protection Regulation (GDPR), which aims to ensure patient's privacy. Anonymization or removal of patient identifiable information, though the most conventional way, is the first important step to adhere to the regulations and incorporate privacy concerns. In this paper, we review the existing anonymization techniques and their applicability to various types (relational and graph-based) of health data. Besides, we provide an overview of possible attacks on anonymized data. We illustrate via a reconstruction attack that anonymization though necessary, is not sufficient to address patient privacy and discuss methods for protecting against such attacks. Finally, we discuss tools that can be used to achieve anonymization.