Utility-preserving anonymization for health data publishing

BMC Medical Informatics and Decision Making

Research Article

Yon Dohn Chung¹  Hyukki Lee¹  Soohyung Kim²  Jong Wook Kim³ 

[1] Department of Computer Science and Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, 02841, Seoul, Republic of Korea;Department of IT Convegence, Korea University, 145 Anam-ro, Seongbuk-gu, 02841, Seoul, Republic of Korea;Department of Media Software, 20-Gil, Hongji-dong, Seongbuk-gu, 03016, Seoul, Republic of Korea;

关键词: Medical privacy;    Data anonymization;    Utility-preserving data publishing;    K-anonymity;   

DOI  :  10.1186/s12911-017-0499-0

 received in 2017-04-06, accepted in 2017-06-28,  发布年份 2017

来源: Springer

【 摘 要 】

BackgroundPublishing 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.MethodsWe 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.ResultsWith 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.ConclusionsWe 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.

【 授权许可】

CC BY   
© The Author(s) 2017

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