A Review of Anonymization for Healthcare Data
Open accessAbstract
Mining health data can lead to faster medical decisions, improvement in the quality of treatment, disease prevention, and reduced cost, and it drives innovative solutions within the healthcare sector. However, health data are highly sensitive and subject to regulations such as the General Data Protection Regulation, which aims to ensure patient's privacy. Anonymization or removal of patient identifiable information, although the most conventional way, is the first important step to adhere to the regulations and incorporate privacy concerns. In this article, 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, although 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.
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Synthea generates realistic but not real medical data, mimicking accurate patient data in structure and composition without corresponding to individual patient information.
“Synthea, tailored explicitly for research purposes, generates 'realistic but not real medical data', mimicking accurate patient data in structure and composition without corresponding to individual patient information [23].”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [23]
Synthetic data generation is vital for maintaining patient privacy and the integrity of AI models, enabling development and testing of healthcare AI applications without the risk of exposing sensitive patient information.
“This innovative approach is vital for maintaining patient privacy and the integrity of AI models, enabling the development and testing of healthcare AI applications without the risk of exposing sensitive patient information.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [23]
Synthea and similar tools allow for developing and refining AI models, conducting analyses, and exploring new healthcare technologies while safeguarding patient privacy.
“By providing rich datasets, Synthea and similar tools allow for developing and refining AI models, conducting analyses, and exploring new healthcare technologies while safeguarding patient privacy.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [23]
Synthetic data generation is pivotal in ensuring the privacy and security of healthcare data, especially in AI applications, as it allows for the use of comprehensive and realistic datasets without compromising patient confidentiality.
“Synthetic data generation is pivotal in ensuring the privacy and security of healthcare data, especially in AI applications, as it allows for the use of comprehensive and realistic datasets without compromising patient confidentiality.”
From Balancing Privacy and Progress: A Review of Privacy Challenges, Systemic Oversight, and Patient Perceptions in AI-Driven Healthcare (2024) · cites this paper as [23]
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Notice of Removal February 26, 2026: FAST: Fast Anonymization of Big Data Streams
Mohammadian, E., Noferesti, M., & Jalili, R. (2014) · International Conference on Big Data Science and Computing (BigDataScience) · doi:10.1145/2640087.2644149 · Cited by 26 · see citing works
A data recipient centered de-identification method to retain statistical attributes
Gal, T. S., Tucker, T. C., Gangopadhyay, A., & Chen, Z. (2014) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2014.01.001 · Cited by 25 · see citing works
Extending ℓ-diversity to generalize sensitive data
Tian, H., & Zhang, W. (2010) · Data & Knowledge Engineering · doi:10.1016/j.datak.2010.09.001 · Cited by 23 · see citing works
Constructing Biomedical Knowledge Graph Based on SemMedDB and Linked Open Data
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A Complete (alpha,k)-Anonymity Model for Sensitive Values Individuation Preservation
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A Novel Differential Privacy Approach that Enhances Classification Accuracy
Zaman, A., Obimbo, C., & Dara, R. A. (2016) · doi:10.1145/2948992.2949027 · Cited by 21 · see citing works
Privacy Preserving Data Publishing for Multiple Sensitive Attributes Based on Security Level
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Privacy-Preserving Data Publication with Features of Independent -Diversity
Zhu, H. P., Tian, S., & Lu, K. (2014) · The Computer Journal · doi:10.1093/comjnl/bxu102 · Cited by 19 · see citing works
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Privacy and the Dimensionality Curse
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Decision support for releasing anonymised data
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An Approach for Distributing Sensitive Values in k-Anonymity
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Privacy Preserving Data Publishing with Multiple Sensitive Attributes based on Overlapped Slicing
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Privacy-Preserving Big Data Publication: (K, L) Anonymity
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Privacy Preserving k-Anonymity for Re-publication of Incremental Datasets
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$X-BAND$ : Expiration Band for Anonymizing Varied Data Streams
Otgonbayar, A., Pervez, Z., & Dahal, K. P. (2019) · IEEE Internet of Things Journal · doi:10.1109/jiot.2019.2955435 · Cited by 9 · see citing works
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Membership Inference Attack on Graph Neural NetworksPreprint
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Self-citationAn Enhanced Method for Privacy-Preserving Data Publishing
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You Can't See Me: Anonymizing Graphs Using the Szemerédi Regularity Lemma
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Flagged first, then by citations
Research works printed but not linked: 11
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Cited by
Sorted by citations
Williamson, S. M., & Prybutok, V. (2024) · Applied Sciences · doi:10.3390/app14020675 · Cited by 567 · see citing works
Clay, B., Bergman, H. I., Salim, S., Pergola, G., Shalhoub, J., & Davies, A. H. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.109808 · Cited by 41 · see citing works
- Authors
- Olatunji, I. E., Rauch, J., Katzensteiner, M., & Khosla, M.
- Affiliations
- Leibniz University Hannover (DE)
- Hochschule Osnabrück (DE)
- Hochschule Hannover (DE)
- Corresponding author
- Olatunji, Iyiola E.
- Venue
- Big Data
- Publisher
- SAGE Publications
- Published
- 2024-12-01
- Volume
- 12
- Issue
- 6
- Pages
- 538-555
- ISSN
- 2167-6461 (ISSN-L)
- 2167-647X
- OpenAlex
- W3153562187
- Cited by
- 101
- References
- 123
- Citations by year
- 2026: 20 · 2025: 29 · 2024: 30 · 2023: 13 · 2022: 9
- FWCI
- 1.41
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Big Data (publishedVersion)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University) (cc-by)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Physical Sciences › Computer Science › Information Systems › Thoreau and American Literature
- Social Sciences › Decision Sciences › Management Science and Operations Research › Data Quality and Management
- Keywords
- data anonymization, healthcare data privacy, patient privacy, General Data Protection Regulation, reconstruction attacks, relational data, privacy-preserving data mining
- MeSH terms
- Data Anonymization, Confidentiality, Humans, Computer Security
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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