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http://dx.doi.org/10.33778/kcsa.2022.22.4.085

A study on the policy of de-identifying unstructured data for the medical data industry  

Sun-Jin Lee (성신여자대학교 미래융합기술공학과)
Tae-Rim Park (성신여자대학교 미래융합기술공학과)
So-Hui Kim (신세계아이앤씨 정보보안팀)
Young-Eun Oh (라인비즈플러스 viz security)
Il-Gu Lee (성신여자대학교 융합보안공학과 / 미래융합기술공학과)
Publication Information
Convergence Security Journal / v.22, no.4, 2022 , pp. 85-97 More about this Journal
Abstract
With the development of big data technology, data is rapidly entering a hyperconnected intelligent society that accelerates innovative growth in all industries. The convergence industry, which holds and utilizes various high-quality data, is becoming a new growth engine, and big data is fused to various traditional industries. In particular, in the medical field, structured data such as electronic medical record data and unstructured medical data such as CT and MRI are used together to increase the accuracy of disease prediction and diagnosis. Currently, the importance and size of unstructured data are increasing day by day in the medical industry, but conventional data security technologies and policies are structured data-oriented, and considerations for the security and utilization of unstructured data are insufficient. In order for medical treatment using big data to be activated in the future, data diversity and security must be internalized and organically linked at the stage of data construction, distribution, and utilization. In this paper, the current status of domestic and foreign data security systems and technologies is analyzed. After that, it is proposed to add unstructured data-centered de-identification technology to the guidelines for unstructured data and technology application cases in the industry so that unstructured data can be actively used in the medical field, and to establish standards for judging personal information for unstructured data. Furthermore, an object feature-based identification ID that can be used for unstructured data without infringing on personal information is proposed.
Keywords
Medical industry; Unstructured data; De-identification; De-identification action; De-identification policy;
Citations & Related Records
Times Cited By KSCI : 5  (Citation Analysis)
연도 인용수 순위
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