mlsanityData to 7 Oct 2026

A Review of Anonymization for Healthcare Data

arXiv 2104.06523 ↗First posted 13 Apr 2021cs.CR cs.LGcited by 12

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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.

Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). Builds on 6 of its 131 references that are in the map. First posted on arXiv 13 Apr 2021.

Builds on

6 of 131 references.

References · 6

  1. 2016Deep Learning with Differential Privacycited by 2,682
  2. 2018A generic framework for privacy preserving deep learningcited by 103
  3. 2021Membership Inference Attack on Graph Neural Networkscited by 81
  4. 2018Linear Program Reconstruction in Practicecited by 12
  5. 2016Individual Differential Privacy: A Utility-Preserving Formulation of Differential Privacy Guaranteescited by 10
  6. 2015t-Closeness through Microaggregation: Strict Privacy with Enhanced Utility Preservationcited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 5; 2024: 1; 2025: 3; 2026: 2.

12 papers.

  1. 2022A Survey on Medical Document Summarizationcited by 14
  2. 2025PIIvot: A Lightweight NLP Anonymization Framework for Question-Anchored Tutoring Dialoguescited by 10
  3. 2023Does Black-box Attribute Inference Attacks on Graph Neural Networks Constitute Privacy Risk?cited by 7
  4. 2024Protect and Extend -- Using GANs for Synthetic Data Generation of Time-Series Medical Recordscited by 4
  5. 2023MLHOps: Machine Learning for Healthcare Operationscited by 2
  6. 2023From Principle to Practice: Vertical Data Minimization for Machine Learningcited by 2
  7. 2025Beyond the Generative Learning Trilemma: Generative Model Assessment in Data Scarcity Domainscited by 1
  8. 2026SoK: Privacy-aware LLM in Healthcare: Threat Model, Privacy Techniques, Challenges and Recommendationscited by 1
  9. 2023Federated Learning on Patient Data for Privacy-Protecting Polycystic Ovary Syndrome Treatmentcited by 0
  10. 2023Statistical properties and privacy guarantees of an original distance-based fully synthetic data generation methodcited by 0
  11. 2025Efficient and Privacy-Preserved Link Prediction via Condensed Graphscited by 0
  12. 2026A Pipeline for Generating Longitudinal Synthetic Clinical Notes Using Large Language Modelscited by 0