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Utility-preserving anonymization for health data publishing

Overview of attention for article published in BMC Medical Informatics and Decision Making, July 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (82nd percentile)
  • High Attention Score compared to outputs of the same age and source (87th percentile)

Mentioned by

news
1 news outlet
twitter
2 tweeters

Citations

dimensions_citation
16 Dimensions

Readers on

mendeley
35 Mendeley
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Title
Utility-preserving anonymization for health data publishing
Published in
BMC Medical Informatics and Decision Making, July 2017
DOI 10.1186/s12911-017-0499-0
Pubmed ID
Authors

Hyukki Lee, Soohyung Kim, Jong Wook Kim, Yon Dohn Chung

Abstract

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. 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. 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. 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.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 35 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 35 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 8 23%
Student > Master 7 20%
Other 5 14%
Student > Bachelor 3 9%
Researcher 2 6%
Other 4 11%
Unknown 6 17%
Readers by discipline Count As %
Computer Science 16 46%
Medicine and Dentistry 6 17%
Engineering 2 6%
Social Sciences 1 3%
Materials Science 1 3%
Other 1 3%
Unknown 8 23%

Attention Score in Context

This research output has an Altmetric Attention Score of 11. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 15 March 2019.
All research outputs
#1,943,264
of 16,291,665 outputs
Outputs from BMC Medical Informatics and Decision Making
#171
of 1,478 outputs
Outputs of similar age
#38,162
of 217,768 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#1
of 8 outputs
Altmetric has tracked 16,291,665 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,478 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one has done well, scoring higher than 88% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 217,768 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 82% of its contemporaries.
We're also able to compare this research output to 8 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them