BMC Medical Informatics and Decision Making | |
Utility-preserving anonymization for health data publishing | |
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
【 预 览 】
Files | Size | Format | View |
---|---|---|---|
RO202311091120847ZK.pdf | 1332KB | download | |
12864_2015_2137_Article_IEq11.gif | 1KB | Image | download |
12864_2017_4363_Article_IEq6.gif | 1KB | Image | download |
12864_2016_2682_Article_IEq31.gif | 1KB | Image | download |
12645_2016_25_Article_IEq10.gif | 1KB | Image | download |
12864_2017_3783_Article_IEq2.gif | 1KB | Image | download |
12864_2017_3783_Article_IEq3.gif | 1KB | Image | download |
12864_2017_3783_Article_IEq4.gif | 1KB | Image | download |
12864_2016_2682_Article_IEq35.gif | 1KB | Image | download |
12864_2016_3263_Article_IEq14.gif | 1KB | Image | download |
12864_2016_2888_Article_IEq7.gif | 1KB | Image | download |
12864_2016_3263_Article_IEq18.gif | 1KB | Image | download |
12864_2017_3655_Article_IEq20.gif | 1KB | Image | download |
12864_2016_2682_Article_IEq39.gif | 1KB | Image | download |
12864_2017_3655_Article_IEq22.gif | 1KB | Image | download |
12864_2016_2821_Article_IEq7.gif | 1KB | Image | download |
12864_2017_4020_Article_IEq33.gif | 1KB | Image | download |
12864_2015_2198_Article_IEq46.gif | 1KB | Image | download |
12864_2017_4025_Article_IEq9.gif | 1KB | Image | download |
12864_2015_2296_Article_IEq99.gif | 1KB | Image | download |
【 图 表 】
12864_2015_2296_Article_IEq99.gif
12864_2017_4025_Article_IEq9.gif
12864_2015_2198_Article_IEq46.gif
12864_2017_4020_Article_IEq33.gif
12864_2016_2821_Article_IEq7.gif
12864_2017_3655_Article_IEq22.gif
12864_2016_2682_Article_IEq39.gif
12864_2017_3655_Article_IEq20.gif
12864_2016_3263_Article_IEq18.gif
12864_2016_2888_Article_IEq7.gif
12864_2016_3263_Article_IEq14.gif
12864_2016_2682_Article_IEq35.gif
12864_2017_3783_Article_IEq4.gif
12864_2017_3783_Article_IEq3.gif
12864_2017_3783_Article_IEq2.gif
12645_2016_25_Article_IEq10.gif
12864_2016_2682_Article_IEq31.gif
12864_2017_4363_Article_IEq6.gif
12864_2015_2137_Article_IEq11.gif
【 参考文献 】
- [1]
- [2]
- [3]
- [4]
- [5]
- [6]
- [7]
- [8]
- [9]
- [10]
- [11]
- [12]
- [13]
- [14]
- [15]
- [16]
- [17]
- [18]
- [19]
- [20]
- [21]
- [22]
- [23]
- [24]
- [25]
- [26]