Details
Original language | English |
---|---|
Pages (from-to) | 538-555 |
Number of pages | 18 |
Journal | Big data |
Volume | 12 |
Issue number | 6 |
Early online date | 11 Dec 2024 |
Publication status | Published - Dec 2024 |
Abstract
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.
Keywords
- anonymization, attacks, healthcare data, privacy
ASJC Scopus subject areas
- Computer Science(all)
- Information Systems
- Computer Science(all)
- Computer Science Applications
- Decision Sciences(all)
- Information Systems and Management
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In: Big data, Vol. 12, No. 6, 12.2024, p. 538-555.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - A Review of Anonymization for Healthcare Data
AU - Olatunji, Iyiola E.
AU - Rauch, Jens
AU - Katzensteiner, Matthias
AU - Khosla, Megha
N1 - Publisher Copyright: Copyright 2022, Mary Ann Liebert, Inc., publishers.
PY - 2024/12
Y1 - 2024/12
N2 - 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.
AB - 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.
KW - anonymization
KW - attacks
KW - healthcare data
KW - privacy
UR - http://www.scopus.com/inward/record.url?scp=85212991133&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2104.06523
DO - 10.48550/arXiv.2104.06523
M3 - Article
C2 - 35271377
AN - SCOPUS:85212991133
VL - 12
SP - 538
EP - 555
JO - Big data
JF - Big data
SN - 2167-6461
IS - 6
ER -