Utility-preserving anonymization for health data publishing

Collection with item attached
2017
Item details URL
http://open-repository.kisti.re.kr/cube/handle/open_repository/486802.do
DOI
10.1186/s12911-017-0499-0
Title
Utility-preserving anonymization for health data publishing
Description
This work was supported by Institute for Information & communicationsTechnology Promotion(IITP) grant funded by the Korea government(MSIP)(No. R0190-15-2019, Development of personal information protectiontechnology using unidentifiability technique on big data environment).
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.
provenance
Made available in Cube on 2018-09-28T16:17:20Z (GMT). No. of bitstreams: 0
language
English
author
Lee, Hyukki
Kim, Soohyung
Kim, Jong Wook
Chung, Yon Dohn
accessioned
2018-09-28T16:17:20Z
available
2018-09-28T16:17:20Z
issued
2017
citation
BMC MEDICAL INFORMATICS AND DECISION MAKING(17)
issn
1472-6947
uri
http://open-repository.kisti.re.kr/cube/handle/open_repository/486802.do
Funder
과학기술정보통신부
Funding Program
SW컴퓨팅산업원천기술개발
Project ID
1711056735
Jurisdiction
Rep.of Korea
Project Name
Development of personal information protection technology using unidentifiability technique on big data environment
rights
openAccess
subject
Medical privacy
Data anonymization
Utility-preserving data publishing
K-anonymity
type
article


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