Detailed Information

Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

Utility-preserving anonymization for health data publishing

Authors
Lee, HyukkiKim, SoohyungKim, Jong WookChung, Yon Dohn
Issue Date
11-7월-2017
Publisher
BMC
Keywords
Medical privacy; Data anonymization; Utility-preserving data publishing; K-anonymity
Citation
BMC MEDICAL INFORMATICS AND DECISION MAKING, v.17
Indexed
SCIE
SCOPUS
Journal Title
BMC MEDICAL INFORMATICS AND DECISION MAKING
Volume
17
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/82839
DOI
10.1186/s12911-017-0499-0
ISSN
1472-6947
Abstract
Background: Publishing raw electronic health records (EHRs) may be considered as a breach of the privacy of individuals because they usually contain sensitive information. A common practice for the privacy- preserving data publishing is to anonymize the data before publishing, and thus satisfy privacy models such as k-anonymity. Among various anonymization techniques, generalization is the most commonly used in medical/health data processing. Generalization inevitably causes information loss, and thus, various methods have been proposed to reduce information loss. However, existing generalization-based data anonymization methods cannot avoid excessive information loss and preserve data utility. Methods: We propose a utility-preserving anonymization for privacy preserving data publishing (PPDP). To preserve data utility, the proposed method comprises three parts: (1) utility-preserving model, (2) counterfeit record insertion, (3) catalog of the counterfeit records. We also propose an anonymization algorithm using the proposed method. Our anonymization algorithm applies full-domain generalization algorithm. We evaluate our method in comparison with existence method on two aspects, information loss measured through various quality metrics and error rate of analysis result. Results: With all different types of quality metrics, our proposed method show the lower information loss than the existing method. In the real-world EHRs analysis, analysis results show small portion of error between the anonymized data through the proposed method and original data. Conclusions: We propose a new utility-preserving anonymization method and an anonymization algorithm using the proposed method. Through experiments on various datasets, we show that the utility of EHRs anonymized by the proposed method is significantly better than those anonymized by previous approaches.
Files in This Item
There are no files associated with this item.
Appears in
Collections
Graduate School > Department of Computer Science and Engineering > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Researcher CHUNG, YON DOHN photo

CHUNG, YON DOHN
컴퓨터학과
Read more

Altmetrics

Total Views & Downloads

BROWSE