• The Journal of Society for e-Business Studies
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• v.24 no.1
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• pp.121-137
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• 2019

De-identification is a method by which the remaining information can not be referred to a specific individual by removing the personal information from the data set. As a result, de-identification can lower the exposure risk of personal information that may occur in the process of collecting, processing, storing and distributing information. Although there have been many studies in de-identification algorithms, protection models, and etc., most of them are limited to structured data, and there are relatively few considerations on de-identification of unstructured data. Especially, in the medical field where the unstructured text is frequently used, many people simply remove all personally identifiable information in order to lower the exposure risk of personal information, while admitting the fact that the data utility is lowered accordingly. This study proposes a new method to perform de-identification by applying the k-anonymity protection model targeting unstructured text in the medical field in which de-identification is mandatory because privacy protection issues are more critical in comparison to other fields. Also, the goal of this study is to propose a new utility metric so that people can comprehend de-identified data set utility intuitively. Therefore, if the result of this research is applied to various industrial fields where unstructured text is used, we expect that we can increase the utility of the unstructured text which contains personal information.

• Convergence Security Journal
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• v.19 no.5
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• pp.75-84
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• 2019

Recently, the value of video as an important data of medical information technology is increasing due to the feature of rich clinical information. On the other hand, video is also required to be de-identified as a medical image, but the existing methods are mainly specialized in the stereotyped data and still images, which makes it difficult to apply the existing methods to the video data. In this paper, we propose an automated system to index candidate elements of personal identification information on a frame basis to solve this problem. The proposed system performs indexing process using text and person detection after preprocessing by scene segmentation and color knowledge based method. The generated index information is provided as metadata according to the purpose of use. In order to verify the effectiveness of the proposed system, the indexing speed was measured using prototype implementation and real surgical video. As a result, the work speed was more than twice as fast as the playing time of the input video, and it was confirmed that the decision making was possible through the case of the production of surgical education contents.