• 한국IT서비스학회지
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• 제15권4호
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• pp.41-62
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• 2016

In Internet of Things (IoT) era, more diverse types of information are collected and the environment of information usage, distribution, and processing is changing. Recently, there have been a growing number of cases involving breach and infringement of personal information in IoT services, for examples, including data breach incidents of Web cam service or drone and hacking cases of smart connected car or individual monitoring service. With the evolution of IoT, concerns on personal information protection has become a crucial issue and thus the risk analysis and management method of personal information should be systematically prepared. This study shows risk factors in IoT regarding possible breach of personal information and infringement of privacy. We propose "a risk analysis framework of protecting personal information in IoT environments" consisting of asset (personal information-type and sensitivity) subject to risk, threats of infringement (device, network, and server points), and social impact caused from the privacy incident. To verify this proposed framework, we conducted risk analysis of IoT services (smart communication device, connected car, smart healthcare, smart home, and smart infra) using this framework. Based on the analysis results, we identified the level of risk to personal information in IoT services and suggested measures to protect personal information and appropriately use it.

• 전자통신동향분석
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• 제38권5호
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• pp.61-70
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• 2023

We investigated technological trends in an electrocardiogram (ECG)-based biometric cryptosystem that uses physiological features of ECG signals to provide personally identifiable cryptographic key generation and authentication services. The following technical details of the cryptosystem were investigated and analyzed: preprocessing of ECG signals, extraction of personally identifiable features, generation of quantified encryption keys from ECG signals, reproduction of ECG encryption keys under time-varying noise, and new security applications based on ECG signals. The cryptosystem can be used as a security technology to protect users from hacking, information leakage, and malfunctioning attacks in wearable/implantable medical devices, wireless body area networks, and mobile healthcare services.

• 문화기술의 융합
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• 제7권4호
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• pp.577-582
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• 2021

본 연구는 코로나19 팬대믹 이후 외국인 환자의 급감으로 심각하게 타격을 입은 국내 의료관광 산업 회복을 위한 방안을 모색하고자 연구하게 되었다. 전문가 패널 에게 순위별 키워드 자문을 구하여 Kendall의 W검증을 사용하였다. 연구의 결론은 외국인 환자를 유치하는 기관은 중증 외국인 환자 치료 확대로 위기상황을 기회로 바꾸는 계기가 필요하겠다. 또한 가상, 증강현실을 통해 해외 방문이 어려운 상황에서 병원에 대한 친숙함과 신뢰를 얻을 수 있고 언택트 시대에 감염의 위험을 막고 환자를 보호 할 수 있는 원격지료 서비스를 시행함으로써 국내 입국을 하지 않고도 현지에서 의료의 혜택을 누릴 수 있다고 하겠다. 그리고 블록체인은 환자의 정보 보완을 유지하며 안전하게 공유가 가능하고 가상화폐 등을 활용한 지불 수단을 이용해 고객의 불편함을 최소화 할 수 있고 마지막으로 스마트 헬스 케어는 장소와 관계없이 환자를 관리하고 정보를 제공 할 수 있다.

• International Journal of Computer Science & Network Security
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• 제23권10호
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• pp.81-88
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• 2023

In the present scenario, enormous amounts of data are produced every second. These data also contain private information from sources including media platforms, the banking sector, finance, healthcare, and criminal histories. Data mining is a method for looking through and analyzing massive volumes of data to find usable information. Preserving personal data during data mining has become difficult, thus privacy-preserving data mining (PPDM) is used to do so. Data perturbation is one of the several tactics used by the PPDM data privacy protection mechanism. In Perturbation, datasets are perturbed in order to preserve personal information. Both data accuracy and data privacy are addressed by it. This paper will explore and compare several perturbation strategies that may be used to protect data privacy. For this experiment, two perturbation techniques based on random projection and principal component analysis were used. These techniques include Improved Random Projection Perturbation (IRPP) and Enhanced Principal Component Analysis based Technique (EPCAT). The Naive Bayes classification algorithm is used for data mining approaches. These methods are employed to assess the precision, run time, and accuracy of the experimental results. The best perturbation method in the Nave-Bayes classification is determined to be a random projection-based technique (IRPP) for both the cardiovascular and hypothyroid datasets.

• 정보처리학회논문지:컴퓨터 및 통신 시스템
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• 제3권8호
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• pp.245-250
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• 2014

최근 의료기술의 발전으로 인하여 응급의료 서비스가 병원에서 가정으로 변화되고 있다. 이와 관련하여 예방 또는 조기진단을 위한 연구가 활발해지고 있다. 특히, 생체신호를 모니터링하여 다양한 u-헬스케어 응용 서비스에 적용하고 있다. 본 논문에서 제안하는 시스템은 다양한 센서로부터 측정된 의료정보 보호 및 보안 기술을 제안하고자 한다. 특히, 생체신호는 개인의 주요 정보이면서 프라이버시와 관련된 정보이기 때문에 보호 및 관리를 위해 2차원 코드화 기술인 QR 코드를 적용하였다. 클라이언트 단말에서는 QR 코드를 분석하여 확인할 수 있도록 하였다. 끝으로 제안한 플랫폼 상에서 의료영상정보와 생체신호의 통합 이미지 파일 생성과 배포를 확인하는 응용서비스를 통해 수행 결과를 보였다.

• 한국산학기술학회논문지
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• 제15권3호
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• pp.1740-1748
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• 2014

개인 맞춤형 헬스 케어 서비스 플랫폼은 개인별 건강관리를 위하여 다양한 건강정보 데이터를 저장 관리하고 건강정보 제공 및 건강 가이드 서비스를 제공하기 위한 헬스 케어 서비스 플랫폼으로 개인의 프라이버시와 개인정보보호를 전제조건으로 서비스 되어야 한다. 본 논문에서는 개인의 역할에 따른 보안서비스를 기반으로 제공되는 개인 맞춤형 헬스케어 서비스 플랫폼을 제안한다. 제안된 개인 맞춤형 헬스 케어 서비스 플랫폼은 병원에서 측정하는 기초 임상, 영상, 약물 데이터에 대한 저장 및 관리뿐만 아니라 개인적으로 획득할 수 있는 건강 정보, 즉 스마트 기기에서 측정 및 입력 가능한 음식물, 수면, 감정, 운동에 대한 데이터를 종합적으로 관리하여 일상생활에서 개인별 건강정보를 관리할 수 있도록 하여 현대인들의 스마트한 건강생활을 지원하는 기능을 제공하는 서비스 플랫폼에 역할기반 접근제어 모델을 적용하여 개인의 사생활 및 개인 정보 침해를 방지할 수 있도록 하였다. 제안된 RBAC 기반의 스마트 헬스 케어 서비스 플랫폼은 구현환경에 따라서 다양한 사용자와 사용자의 역할 및 권한 등을 설정하여 유연하게 개인 정보보호를 할 수 있는 시스템으로 구축되어 개인 맞춤형 건강정보 서비스를 제공할 수 있다.

• 대한간호학회지
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• 제52권2호
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• pp.134-143
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• 2022

Purpose: The study aimed to understand the delirium experience of intensive care unit (ICU) patients. Methods: We performed a qualitative study using Colaizzi's phenomenological method. Eleven patients, who experienced delirium according to the Confusion Assessment Method for ICU, participated after transferring to general wards from the ICU. Individual in-depth semi-structured interviews ranging from 30 minutes to 2 hours in length were conducted between November 2018 and August 2019. Results: Nine themes and four theme clusters emerged. The four theme clusters were: 1) "Overwhelmed by fear," which describes the experience of a patient close to death and the feeling of difficulty in understanding disorganized thinking; 2) "Anxious about not understanding the situation," which means that patients' sense of time and space were disordered in the ICU; 3) "Being deserted," which indicates the feeling of being separated from others and yourself; and 4) "Resistance to protect my dignity," which indicates that the dignity and autonomy of an individual in the patient's position at the ICU, are ignored. Conclusion: Nursing interventions are needed that would enable patients to maintain orientation and self-esteem in the ICU. In addition, healthcare providers need to provide information about the unfamiliar environment in the ICU in advance.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제16권2호
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• pp.742-756
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• 2022

A flood of information has occurred with the rise of the internet and digital devices in the fourth industrial revolution era. Every millisecond, massive amounts of structured and unstructured data are generated; smartphones, wearable devices, sensors, and self-driving cars are just a few examples of devices that currently generate massive amounts of data in our daily. Machine learning has been considered an approach to support and recognize patterns in data in many areas to provide a convenient way to other sectors, including the healthcare sector, government sector, banks, military sector, and more. However, the conventional machine learning model requires the data owner to upload their information to train the model in one central location to perform the model training. This classical model has caused data owners to worry about the risks of transferring private information because traditional machine learning is required to push their data to the cloud to process the model training. Furthermore, the training of machine learning and deep learning models requires massive computing resources. Thus, many researchers have jumped to a new model known as "Federated Learning". Federated learning is emerging to train Artificial Intelligence models over distributed clients, and it provides secure privacy information to the data owner. Hence, this paper implements Federated Averaging with a Deep Neural Network to classify the handwriting image and protect the sensitive data. Moreover, we compare the centralized machine learning model with federated averaging. The result shows the centralized machine learning model outperforms federated learning in terms of accuracy, but this classical model produces another risk, like privacy concern, due to the data being stored in the data center. The MNIST dataset was used in this experiment.

• 실천공학교육논문지
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• 제16권4호
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• pp.491-497
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• 2024

최근 5년간 10대와 20대에서 부정맥 심장 질환 환자의 비율이 증가하고 있다. 심장 질환이 우리나라 사망원인 2위를 꾸준히 유지하며 수가 증가함에 따라 부정맥을 통한 심전도 검사가 중요해졌으나 심전도 전문 의료기기의 경우 경제적으로 부담이 되고, 큰 부피와 작동이 어려워 개인별 소장이 힘들다는 문제점으로 병원 방문을 통해 검사가 진행된다. 따라서 본 연구에서는 AD8232 센서를 활용하여 데이터를 측정하고, 실시간 모니터링을 통해 생체신호의 변화를 파악할 수 있도록 제공한다. 또한 개인의 민감정보 보호를 위해 세션 및 사용자 인증을 SSL기반으로 개인정보를 보호할 수 있는 개인 맞춤형 웹 서비스를 개발하였다.

• Asian Pacific Journal of Cancer Prevention
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• 제17권1호
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• pp.275-280
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• 2016

Background: A tobacco-free workplace policy is identified as an effective means to reduce tobacco use and protect people from second-hand smoke; however, the number of tobacco-free policies (TFP) remains very low in workplaces in Malaysia. This study explored the factors affecting support for a tobacco-free policy on two healthcare campuses in Malaysia, prior to the implementation of TFP. Materials and Methods: This cross-sectional study was conducted among 286 non-smokers from two healthcare training centres and two nearby colleges in Malaysia from January 2015 to April 2015. A standardized questionnaire was administered via staff and student emails. The questionnaire collected information on sociodemographic characteristics, support for a tobacco-free policy and perceived respiratory and sensory symptoms due to tobacco exposure. Bivariate and multivariate logistic regression analyses were performed to estimate the independent effects of supporting a tobacco-free campus. Results: The percentage of individuals supporting completely tobacco-free facilities was 83.2% (N=238), as opposed to 16.7% (N=48) in support of partially tobacco-free facilities. Compared to the supporters of partially tobacco-free facilities, non-smokers who supported completely tobacco-free health facilities were more likely to be female, have higher education levels, to be very concerned about the effects of other people smoking on their health and to perceive a tobacco-free policy as very important. In addition, they perceived that tobacco smoke bothered them at work by causing headaches and coughs and, in the past 4 weeks, had experienced difficulty breathing. In the multivariate model, after adjusting for sociodemographic characteristics and other factors, only experiencing coughs and headaches increased the odds of supporting a completely tobacco-free campus, up to 2.5- and 1.9-fold, respectively. Conclusions: Coughs and headaches due to other people smoking at work enhances support for a completely tobacco-free campus among non-smokers.