• 한국전자거래학회지
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• 제24권1호
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• pp.121-137
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• 2019

비식별화는 데이터셋으로부터 개인정보를 제거함으로써 개인을 식별할 수 없도록 하는 방법으로, 정보를 수집, 가공, 저장, 배포하는 과정에서 발생할 수 있는 개인정보 노출 위험도를 낮추기 위해 사용한다. 그간 비식별화와 관련된 알고리즘, 모델 등의 관점에서 많은 연구가 이루어졌지만, 대부분은 정형 데이터를 대상으로 하는 제한적인 연구로, 비정형 데이터에 대한 고려는 상대적으로 많지 않은 실정이다. 특히 비정형 텍스트가 빈번히 사용되는 의료 분야의 경우에서는 개인 식별 정보들을 단순 제거함으로써 개인정보 노출 위험도는 낮추지만, 그에 따른 데이터 활용성이 떨어지는 점을 감수하는 실정이다. 본 연구는 개인정보 보호 이슈가 가장 중요하고 따라서 비식별화가 활발하게 연구되고 있는 의료분야 데이터 중 비정형 텍스트를 대상으로 k-익명성 보호모델을 적용한 비식별화 수행 방안을 제시하고, 비식별화 결과에 대한 새로운 유용도 측정 기법을 제안하여 이를 통해 직관적으로 데이터 활용성을 판단할 수 있도록 하는 것을 목표로 한다. 따라서 본 연구의 결과물이 의료 분야뿐만 아니라 비정형 텍스트가 활용되는 모든 산업 분야에서 활용될 경우, 개인 식별 정보가 포함된 비정형 텍스트의 활용도를 향상시킬 수 있을 것으로 기대한다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권3호
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• pp.974-992
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• 2021

Recently, the healthcare field has undergone rapid changes owing to the accumulation of health big data and the development of machine learning. Data mining research in the field of healthcare has different characteristics from those of other data analyses, such as the structural complexity of the medical data, requirement for medical expertise, and security of personal medical information. Various methods have been implemented to address these issues, including the machine learning model and cloud platform. However, the machine learning model presents the problem of opaque result interpretation, and the cloud platform requires more in-depth research on security and efficiency. To address these issues, this paper presents a recent technology for Internet-of-Things-based (IoT-based) health big data processing. We present a cloud-based IoT health platform and health big data processing technology that reduces the medical data management costs and enhances safety. We also present a data mining technology for health-risk prediction, which is the core of healthcare. Finally, we propose a study using explainable artificial intelligence that enhances the reliability and transparency of the decision-making system, which is called the black box model owing to its lack of transparency.

• 한국멀티미디어학회논문지
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• 제22권11호
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• pp.1288-1299
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• 2019

As life expectancy increases, chronic diseases become a problem, and health management in daily life becomes important. With the development of IT convergence technology, personal health records have been collected through various data sources, but they are saved separately by each medical services. Distributed storage of personal health records caused inconvenience about managing the user's health records. In this paper, we designed and implemented an integrated repository and web-based dashboard UI components to solve that inconvenience. The proposed method shows that users can manage their personal health records in the integrated repository effectively and show them through dashboard UI components.

• 예술인문사회 융합 멀티미디어 논문지
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• 제6권7호
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• pp.9-16
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• 2016

국내외적으로 급변하는 인터넷 시대에 생활함에 따라 의료, 금융, 서비스 분야 등에서 대용량 정보가 이용된다. 이에 따라 병원, 기관 등 의료 기관에서의 시스템에서도 개인 정보의 유출 및 외부 침입으로 개인 정보 침해가 발생된다. 의료 기관의 개인건강의료정보의 정보보호 및 개인 프라이버시 보호를 위해 국내외 의료 기관에서는 국가별로 제시한 정책, 법령 기준에 따라 비식별화 처리 기술을 이용하고 있다. 기존 국내외 프라이버시 제도, 법령 등을 비교하여 국내에서 미흡했던 익명화와 가명화 기술 및 대상 데이터 항목에 대해 보다 발전되고 우수한 기술 및 대상을 도출하기 위해 비교 분석한다. 의료 개인정보에 대한 비식별화 처리 기술은 국외 기관인 미국 NIST 및 영국 ICO에서 제시한 국가정보보호를 위한 비식별화 기술에 비해 국내에서는 산학연의 각 기관 및 업체 등에서 자율적으로 비식별화 기술을 채용하고 있는 제약적인 상황이며 국내의 기술은 익명화 기술인 데이터 마스킹이나 삭제 기술의 수준이 되고 있는 실정이다. 국내 개인건강정보의 이용을 활성화하기 위해 재식별화 위험도를 줄인 비식별화 기술인 암호화와 확장성 퍼징 기술을 새롭게 제안하고자 한다.

• 한국정보시스템학회지:정보시스템연구
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• 제29권1호
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• pp.93-111
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• 2020

Purpose The purpose of this paper is to observe the relative priorities of importances among the modified versions of Block chain system, being based on AHP decision support model which should be also proposed in this paper. Design/methodology/approach Four versions modified from the beginning of Block chain were divided into Public& Permissionless, Private&Permissionless, Public&Permissioned and Private&Permissioned types. Five criteria for evaluating the four versions whether the version were suitable for Medical information system were introduced from five factors of Technologies Accept Model, which were Security, Availability, Variety, Reliability and Economical efficiency. We designed Decision support model based on AHP which would select the best alternative version suitable for introducing the Block chain technology into the medical information systems. We established the objective of the AHP model into finding the best choice among the four modified versions. First low layer of the model contains the five factors which consisted of Security, Availability, Variety, Reliability and Economical efficiency. Second low layer of the model contains the four modified versions which consisted Public&Permissionless, Private&Permissionless, Public&Permissioned and Private& Permissioned types. The structural questionnaire based on the AHP decision support model was designed and used to survey experts of medical areas. The collected data by the question investigation was analyzed by AHP analysis technique. Findings The importance priority of Security was highest among five factors of Technologies Accept Mode in the first layer. The importance priority of Private&Permissioned type was highest among four modified versions of Block chain technologies in second low layer. The second importance priority was Private&Permissionless type. The strong point of Private&Permissioned type is to be able to protect personal information and have faster processing speeds. The advantage of Private& Permissionless type is to be also able to protect personal information as well as from forging and altering transaction data. We recognized that it should be necessary to develop new Block chain technologies that would enable to have faster processing speeds as well as from forging and altering transaction data.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2017년도 추계학술대회
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• pp.552-555
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• 2017

빅데이타의 활용과 개인정보보호의 균형점을 찾기 위해 등장한 것이 비식별화이다. 특히 다양한 준식별자 정보 및 민감정보를 처리하는 의료분야에서는 EMR 및 음성, 카카오톡과 같은 의료 상담, SNS 등의 자료 사용을 위해서는 반드시 비식별화를 하여야 한다. 하지만 이를 위한 독립된 의료정보 보호법 및 비식별화를 위한 법제화도 되어 있지 않는 상황이다. 따라서 본 연구에서 국내외 개인정보 비식별화 현황, 의료정보 비식별화 현황 및 사례 그리고 의료정보 보호와 비식별화를 위한 해결과제와 이슈를 제시한다.

• 한국IT서비스학회지
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• 제21권4호
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• pp.63-74
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• 2022

Globally researchers at medical institutions are actively sharing COHORT data of patients to develop vaccines and treatments to overcome the COVID-19 crisis. OMOP-CDM, a common data model that efficiently shares medical data research independently operated by individual medical institutions has patient personal information (e.g. PII, PHI). Although PII and PHI are managed and shared indistinguishably through de-identification or anonymization in medical institutions they could not be guaranteed at 100% by complete de-identification and anonymization. For this reason the security of the OMOP-CDM database is important but there is no detailed and specific OMOP-CDM security inspection tool so risk mitigation measures are being taken with a general security inspection tool. This study intends to study and present a model for implementing a tool to check the security vulnerability of OMOP-CDM by analyzing the security guidelines for the US database and security controls of the personal information protection of the NIST. Additionally it intends to verify the implementation feasibility by real field demonstration in an actual 3 hospitals environment. As a result of checking the security status of the test server and the CDM database of the three hospitals in operation, most of the database audit and encryption functions were found to be insufficient. Based on these inspection results it was applied to the optimization study of the complex and time-consuming CDM CSF developed in the "Development of Security Framework Required for CDM-based Distributed Research" task of the Korea Health Industry Promotion Agency. According to several recent newspaper articles, Ramsomware attacks on financially large hospitals are intensifying. Organizations that are currently operating or will operate CDM databases need to install database audits(proofing) and encryption (data protection) that are not provided by the OMOP-CDM database template to prevent attackers from compromising.

• Asia pacific journal of information systems
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• 제29권4호
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• pp.817-837
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• 2019

The medical industry is rapidly evolving into a combination of artificial intelligence (AI) and ICT technology, such as mobile health, wireless medical, telemedicine and precision medical care. Medical artificial intelligence can be diagnosed and treated, and autonomous surgical robots can be operated. For smart medical services, data such as medical information and personal medical information are needed. AI is being developed to integrate with companies such as Google, Facebook, IBM and others in the health care field. Telemedicine services are also becoming available. However, security issues of medical information for smart medical industry are becoming important. It can have a devastating impact on life through hacking of medical devices through vulnerable areas. Research on medical information is proceeding on the necessity of privacy and privacy protection. However, there is a lack of research on the practical measures for protecting medical information and the seriousness of security threats. Therefore, in this study, we want to confirm the research trend by collecting data related to medical information in recent 5 years. In this study, smart medical related papers from 2014 to 2018 were collected using smart medical topics, and the medical information papers were rearranged based on this. Research trend analysis uses topic modeling technique for topic information. The result constructs topic network based on relation of topics and grasps main trend through topic.

• 한국인터넷방송통신학회논문지
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• 제16권4호
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• pp.109-115
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• 2016

라이프로그는 개인차원에서 일상생활을 오랫동안 기억하거나 공유하기위한 단순한 기록목적으로 시작되었지만 최근 다양한 기업들이 각각의 전문성을 활용한 분석방법을 도입함으로써 개인의 삶의 질이 향상되는 새로운 라이프로 그 비즈니스가 형성되고 있다. 이러한 중요한 장점에도 불구하고 개인 건강 라이프로그 서비스는 데이터의 보안에 관련된 사용자 입장에서는 피할 수 없는 중요한 도전을 제기하고 있다. 개인 건강 라이프로그 서비스가 활성화되면서 사용자 개인정보 침해가 발생하고 사용자의 민감한 의료정보가 유출되는 문제가 증가되고 있다. 본 논문에서는 개인 건강 라이프로그 서비스를 위한 보안 참조모델을 제시하고자 한다. 제안된 보안 참조모델은 건강 라이프로그 서비스 제공을 위한 개인 정보 보호 방안에 명확한 지침을 제시하여 관련 분야의 산업 활성화 및 신 시장 개척을 이끌어 낼 수 있을 것으로 예상된다.

• 의학교육논단
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• 제25권2호
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• pp.84-101
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• 2023

Longitudinal data can provide important evidence with the potential to stimulate innovation and affect policies in medical education and can serve as a driving force for further developments in medical education through evidence-based decisions. Tracking and observing cohorts of students and graduates using longitudinal data can be a way to link the past, present, and future of medical education. This study reviewed practical methods and technical, administrative, and ethical considerations for the establishment and operation of a longitudinal database and presented examples of longitudinal databases. Cohort study design methods and previous examples of research using longitudinal databases to explore major topics in medical education were also reviewed. The implications of this study are as follows: (1) a systematic design process is required to establish longitudinal data, and each university should engage in ongoing deliberation about this issue; (2) efforts are needed to alleviate "survey fatigue" among respondents and reduce the administrative burden of those conducting data collection and analysis; (3) it is necessary to regularly review issues of personal information protection, data security, and ethics regarding the survey respondents; and (4) a system should be established that integrates and manages a longitudinal database of medical education at the national level. The hope is that establishing longitudinal data and cohorts at individual medical schools will not be a temporary phenomenon, but rather that they will be well utilized at the national level to innovate and implement ongoing changes in medical education.