• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2009.10a
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• pp.866-870
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• 2009

Mobile Health (M-Health) system is a recent term for medical and public health practice supported by mobile devices, such as mobile phones, PDAs, and other wireless devices. Mobile Health system has been successfully establishing at few general hospital in Korea. However, to use diverse devices manufactured by various company cause inoperability, and lack of security disappoints customers often. Although the outstanding health environment, most of hospitals are unavailable to share electronic patient records due to lack of standard protocol to handle the interoperability each other. Health Level 7 (HL7) is the best solution for the problem. In this paper, we will analyse a current M-Health service in terms of security and mobile device, and suggest iPhone for the best device against hospital environment. Also, for keep confidentiality of health information and patient privacy, enhanced security mechanism is introduced. As a consequence, interoperable standard, and most appropriate device for supporting staffs and M-Health performance, and enhanced securirty mechanism will be integrated in order to propose improved M-health model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.4
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• pp.826-842
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• 2024

As 5G and AI continue to develop, there has been a significant surge in the healthcare industry. The COVID-19 pandemic has posed immense challenges to the global health system. This study proposes an FL-supported edge computing model based on federated learning (FL) for predicting clinical outcomes of COVID-19 patients during hospitalization. The model aims to address the challenges posed by the pandemic, such as the need for sophisticated predictive models, privacy concerns, and the non-IID nature of COVID-19 data. The model utilizes the FATE framework, known for its privacy-preserving technologies, to enhance predictive precision while ensuring data privacy and effectively managing data heterogeneity. The model's ability to generalize across diverse datasets and its adaptability in real-world clinical settings are highlighted by the use of SHAP values, which streamline the training process by identifying influential features, thus reducing computational overhead without compromising predictive precision. The study demonstrates that the proposed model achieves comparable precision to specific machine learning models when dataset sizes are identical and surpasses traditional models when larger training data volumes are employed. The model's performance is further improved when trained on datasets from diverse nodes, leading to superior generalization and overall performance, especially in scenarios with insufficient node features. The integration of FL with edge computing contributes significantly to the reliable prediction of COVID-19 patient outcomes with greater privacy. The research contributes to healthcare technology by providing a practical solution for early intervention and personalized treatment plans, leading to improved patient outcomes and efficient resource allocation during public health crises.

• Asia pacific journal of information systems
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• v.29 no.4
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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.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.8 no.5
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• pp.235-241
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• 2008

Please Interests in the medical service are increasing in internet environment as life quality of the people improves because of development in information and medical technology. The medical information in today's modern internet environment can violate privacy of the patients. Many medical institutions in Korea are very passive in the privacy protection of patients in the internet environment. The law, standard scheme and systematic guidance to prevent drain of medical information are not developed. This study examines cases of infringement pattern on information of each patient in the internet environment. This study will also try to find a solution to protect the personal information of patients in the internet environment in the measures of law system, technique and management.

• Asia pacific journal of information systems
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• v.18 no.1
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• pp.145-162
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• 2008

One of the big concerns in e-society is privacy issue. In special, in developing robust ubiquitous smart space and corresponding services, user profile and preference are collected by the service providers. Privacy issue would be more critical in context-aware services simply because most of the context data themselves are private information: user's current location, current schedule, friends nearby and even her/his health data. To realize the potential of ubiquitous smart space, the systems embedded in the space should corporate personal privacy preferences. When the users invoke a set of services, they are asked to allow the service providers or smart space to make use of personal information which is related to privacy concerns. For this reason, the users unhappily provide the personal information or even deny to get served. On the other side, service provider needs personal information as rich as possible with minimal personal information to discern royal and trustworthy customers and those who are not. It would be desirable to enlarge the allowable personal information complying with the service provider's request, whereas minimizing service provider's requiring personal information which is not allowed to be submitted and user's submitting information which is of no value to the service provider. In special, if any personal information required by the service provider is not allowed, service will not be provided to the user. P3P (Platform for Privacy Preferences) has been regarded as one of the promising alternatives to preserve the personal information in the course of electronic transactions. However, P3P mainly focuses on preserving the buyers' personal information. From time to time, the service provider's business data should be protected from the unintended usage from the buyers. Moreover, even though the user's privacy preference could depend on the context happened to the user, legacy P3P does not handle the contextual change of privacy preferences. Hence, the purpose of this paper is to propose a mutual P3P-based negotiation mechanism. To do so, service provider's privacy concern is considered as well as the users'. User's privacy policy on the service provider's information also should be informed to the service providers before the service begins. Second, privacy policy is contextually designed according to the user's current context because the nomadic user's privacy concern structure may be altered contextually. Hence, the methodology includes mutual privacy policy and personalization. Overall framework of the mechanism and new code of ethics is described in section 2. Pervasive platform for mutual P3P considers user type and context field, which involves current activity, location, social context, objects nearby and physical environments. Our mutual P3P includes the privacy preference not only for the buyers but also the sellers, that is, service providers. Negotiation methodology for mutual P3P is proposed in section 3. Based on the fact that privacy concern occurs when there are needs for information access and at the same time those for information hiding. Our mechanism was implemented based on an actual shopping mall to increase the feasibility of the idea proposed in this paper. A shopping service is assumed as a context-aware service, and data groups for the service are enumerated. The privacy policy for each data group is represented as APPEL format. To examine the performance of the example service, in section 4, simulation approach is adopted in this paper. For the simulation, five data elements are considered: $\cdot$ UserID $\cdot$ User preference $\cdot$ Phone number $\cdot$ Home address $\cdot$ Product information $\cdot$ Service profile. For the negotiation, reputation is selected as a strategic value. Then the following cases are compared: $\cdot$ Legacy P3P is considered $\cdot$ Mutual P3P is considered without strategic value $\cdot$ Mutual P3P is considered with strategic value. The simulation results show that mutual P3P outperforms legacy P3P. Moreover, we could conclude that when mutual P3P is considered with strategic value, performance was better than that of mutual P3P is considered without strategic value in terms of service safety.

• Journal of Communications and Networks
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• v.13 no.2
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• pp.102-112
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• 2011

In this paper, we propose a privacy-preserving emergency call scheme, called PEC, enabling patients in life-threatening emergencies to fast and accurately transmit emergency data to the nearby helpers via mobile healthcare social networks (MHSNs). Once an emergency happens, the personal digital assistant (PDA) of the patient runs the PEC to collect the emergency data including emergency location, patient health record, as well as patient physiological condition. The PEC then generates an emergency call with the emergency data inside and epidemically disseminates it to every user in the patient's neighborhood. If a physician happens to be nearby, the PEC ensures the time used to notify the physician of the emergency is the shortest. We show via theoretical analysis that the PEC is able to provide fine-grained access control on the emergency data, where the access policy is set by patients themselves. Moreover, the PEC can withstandmultiple types of attacks, such as identity theft attack, forgery attack, and collusion attack. We also devise an effective revocation mechanism to make the revocable PEC (rPEC) resistant to inside attacks. In addition, we demonstrate via simulation that the PEC can significantly reduce the response time of emergency care in MHSNs.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.12
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• pp.4062-4080
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• 2022

COVID-19, a highly infectious disease, has affected the globe tremendously since its outbreak during late 2019 in Wuhan, China. In order to respond to the pandemic, governments around the world introduced a variety of public health measures including contact-tracing, a method to identify individuals who may have come into contact with a confirmed COVID-19 patient, which usually leads to quarantine of certain individuals. Like many other governments, the South Korean health authorities adopted public health measures using latest data technologies. Key data technology-based quarantine measures include:(1) Electronic Entry Log; (2) Self-check App; and (3) COVID-19 Wristband, and heavily relied on individual's personal information for contact-tracing and self-isolation. In fact, during the early stages of the pandemic, South Korea's strategy proved to be highly effective in containing the spread of coronavirus while other countries suffered significantly from the surge of COVID-19 patients. However, while the South Korean COVID-19 policy was hailed as a success, it must be noted that the government achieved this by collecting and processing a wide range of personal information. In collecting and processing personal information, the data minimum principle - one of the widely recognized common data principles between different data protection laws - should be applied. Public health measures have no exceptions, and it is even more crucial when government activities are involved. In this study, we provide an analysis of how the governments around the world reacted to the COVID-19 pandemic and evaluate whether the South Korean government's digital quarantine measures ensured the protection of its citizen's right to privacy.

• Convergence Security Journal
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• v.19 no.5
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• pp.25-36
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• 2019

With the development of information and communication technology, hospitals that electronically process and manage medical information of patients are increasing. However, if medical information is processed electronically, there is still room for infringing personal information of the patient or medical staff. Accordingly, in 2017, the International Organization for Standardization (ISO) published ISO TS 25237 Health Information - Pseudonymization[1]. In this paper, we examine the re - identification process based on ISO TS 25237, the procedure and the problems of our proposed method. In addition, we propose a new processing scheme that adds a re-identification procedure to our secure differential privacy method [2] by keeping a mapping table between de-identified data sets and original data as ciphertext. The proposed method has proved to satisfy the requirements of ISO TS 25237 trust service providers except for some policy matters.

• Information Systems Review
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• v.25 no.1
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• pp.165-187
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• 2023

The continuous increase in life expectancy and high interest in health has brought about significant changes in the use of health information by the public according to the development of information technology represented by the Internet and smartphones. As the medical market expands to the mobile health environment, many health-related apps have been created and distributed, but the acceptance rate is slow as it has become challenging to provide services due to various regulations. In this study, perceived value, perceived risk factors (psychological risk, risk of time-loss, legal risk), and perceived benefits (usefulness, interaction, autonomy) were derived and verified as factors that affect the acceptance resistance of personal health record apps based on the privacy calculation model. In addition, by analyzing the moderating effect of trust in the manufacturer, how the perceived risk and perceived benefit affect the perceived value was verified. A survey was conducted on Korean college students who recognized the personal health record apps but did not use them, and 127 samples were analyzed using structural equations. As a result of hypothesis verification, perceived value has a negative effect on acceptance resistance, perceived risk (risk of time-loss) has a negative effect on perceived value, and perceived benefits (usefulness, interaction, autonomy) were found to have a positive effect on perceived value. Trust in manufacturers has weakened the impact of perceived risks (legal risk) on perceived values. This study is expected to play an important role in maintaining a competitive advantage in the personal health record app market environment by identifying and proposing detailed criteria for reducing the acceptance resistance of personal health record apps.

• International Journal of Computer Science & Network Security
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• v.23 no.12
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• pp.225-234
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• 2023

In this era of information and communication technology (ICT), tremendous improvements have been witnessed in our daily lives. The impact of these technologies is subjective and negative or positive. For instance, ICT has brought a lot of ease and versatility in our lifestyles, on the other hand, its excessive use brings around issues related to physical and mental health etc. In this study, we are bridging these both aspects by proposing the idea of AI based mental healthcare (AIMS). In this regard, we aim to provide a platform where the patient can register to the system and take consultancy by providing their assessment by means of a chatbot. The chatbot will send the gathered information to the machine learning block. The machine learning model is already trained and predicts whether the patient needs a treatment by classifying him/her based on the assessment. This information is provided to the mental health practitioner (doctor, psychologist, psychiatrist, or therapist) as clinical decision support. Eventually, the practitioner will provide his/her suggestions to the patient via the proposed system. Additionally, the proposed system prioritizes care, support, privacy, and patient autonomy, all while using a friendly chatbot interface. By using technology like natural language processing and machine learning, the system can predict a patient's condition and recommend the right professional for further help, including in-person appointments if necessary. This not only raises awareness about mental health but also makes it easier for patients to start therapy.