• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.15 no.6
• /
• pp.3865-3871
• /
• 2014

The U-health service provides medical services with patients anytime or anywhere and is defined as the service that combines information and communication technology with health and medical service. However, it causes some troubles, such as the disclosure of patients' medical information or data spills (personal information extrusion). Moreover, it has the weak point of the security threats associated with data based on existing wire-wireless systems because it conducts data transmission and reception through the network. Therefore, this paper suggests a safe personal information management system by designing integrated certification schema that will help compensate for the weaknesses of the U-health service. In the proposal, the protocols for user information, certification between medical institution and users, data communication encryption & decryption, and user information disuse were designed by applying the ID-Based Encryption, and analyzed such existing systems and PKI Based-based communication process, securely and safely.

• Journal of Internet Computing and Services
• /
• v.22 no.1
• /
• pp.99-107
• /
• 2021

With the recent establishment of a ubiquitous-based medical and healthcare environment, the medical information system for obtaining situation information from various sensors is increasing. In the medical information system environment based on context-awareness, the patient situation can be determined as normal or emergency using situational information. In addition, medical staff can easily access patient information after simple user authentication using ID and Password through applications on smart devices. However, these services of authentication and patient information access are staff-oriented systems and do not fully consider the ubiquitous-based healthcare information system environment. In this paper, we present a authentication service model based context-awareness system for providing situational information-driven authentication services to users who access medical information, and implemented proposed system. The authentication service model based context-awareness system is a service that recognizes patient situations through sensors and the authentication and authorization of medical staff proceed differently according to patient situations. It was implemented using wearables, biometric data measurement modules, camera sensors, etc. to configure various situational information measurement environments. If the patient situation was emergency situation, the medical information server sent an emergency message to the smart device of the medical staff, and the medical staff that received the emergency message tried to authenticate using the application of the smart device to access the patient information. Once all authentication was completed, medical staff will be given access to high-level medical information and can even checked patient medical information that could not be seen under normal situation. The authentication service model based context-awareness system not only fully considered the ubiquitous medical information system environment, but also enhanced patient-centered systematic security and access transparency.

• Journal of Biomedical Engineering Research
• /
• v.29 no.6
• /
• pp.457-465
• /
• 2008

HL7(Health Level 7) is a standard for exchanging medical and healthcare data among different medical information systems. As the ubiquitous era is coming, in addition to text and imaging information, a new type of data, i.e., streaming sensor data appear. Since the HL7 is not covering the interfaces among the devices that produces sensor data, it is expected that sooner or later the HL7 needs to include the biomedical sensors and sensor networks. The IEEE 1451 is a family of standards that deals with the sensors, transducers including sensors and actuators, and various wired or wireless sensor networks. In this paper, we consider the possibility of interoperability between the IEEE 1451 and HL7. After we propose a format of messages in HL7 to include the IEEE 1451 TEDS, we present some preliminary results that show the possibility of integrating the two standards.

• International Journal of Computer Science & Network Security
• /
• v.21 no.8
• /
• pp.254-266
• /
• 2021

The Internet of Medical Things (IoMTs) are to be considered an investment and an improvement to respond effectively and efficiently to patient needs, as it reduces healthcare costs, provides the timely attendance of medical responses, and increases the quality of medical treatment. However, IoMT devices face exposure from several security threats that defer in function and thus can pose a significant risk to how private and safe a patient's data is. This document works as a comprehensive review of modern approaches to achieving security within the Internet of Things. Most of the papers cited here are used been carefully selected based on how recently it has been published. The paper highlights some common attacks on IoMTs. Also, highlighting the process by which secure authentication mechanisms can be achieved on IoMTs, we present several means to detect different attacks in IoMTs

• International Journal of Contents
• /
• v.17 no.4
• /
• pp.1-15
• /
• 2021

Named entity recognition (NER) is a crucial task for NLP, which aims to extract information from texts. To build NER systems, deep learning (DL) models are learned with dictionary features by mapping each word in the dataset to dictionary features and generating a unique index. However, this technique might generate noisy labels, which pose significant challenges for the NER task. In this paper, we proposed DL-dictionary features, and evaluated them on two datasets, including the OntoNotes 5.0 dataset and our new infectious disease outbreak dataset named GFID. We used (1) a Bidirectional Long Short-Term Memory (BiLSTM) character and (2) pre-trained embedding to concatenate with (3) our proposed features, named the Convolutional Neural Network (CNN), BiLSTM, and self-attention dictionaries, respectively. The combined features (1-3) were fed through BiLSTM - Conditional Random Field (CRF) to predict named entity classes as outputs. We compared these outputs with other predictions of the BiLSTM character, pre-trained embedding, and dictionary features from previous research, which used the exact matching and partial matching dictionary technique. The findings showed that the model employing our dictionary features outperformed other models that used existing dictionary features. We also computed the F1 score with the GFID dataset to apply this technique to extract medical or healthcare information.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
• /
• v.10 no.5
• /
• pp.417-423
• /
• 2017

This paper intend to implement monitoring systems for individual customized diagnostics to maintain ongoing disease management to promote human health. Analyze the threshold of a measured biological signal using a number of measuring sensors. Performance assessment revealed that the SVM algorithm for bio-signal analysis showed an average error rate of 2 %. The accuracy of the classification is 97.2%, and reduced the maximum of 19.2% of the storage space when you split the window into 5,000 pieces. Out of the total 5,000 bio-signals, 84 results showed that results from the system were differently the results of the expert's diagnosis and showed about 98 % accuracy. However, the results of the monitoring system did not occur when the results of the monitoring system were lower than that of experts. And About 98% accuracy was shown.

• Journal of Information Technology Services
• /
• v.20 no.5
• /
• pp.119-136
• /
• 2021

Since the global spread of COVID-19, social distancing and untact service implementation have spread rapidly. With the transition to a non-face-to-face environment such as telework and remote classes, cyber security threats have increased, and a lot of cyber compromises have also occurred. In this study, cyber-attacks and response cases related to COVID-19 are summarized in four aspects: cyber fraud, cyber-attacks on companies related to COVID-19 and healthcare sector, cyber-attacks on untact services such as telework, and preparation of untact services security for post-covid 19. After the outbreak of the COVID-19 pandemic, related events such as vaccination information and payment of national disaster aid continued to be used as bait for smishing and phishing. In the aspect of cyber-attacks on companies related to COVID-19 and healthcare sector, we can see that the damage was rapidly increasing as state-supported hackers attack those companies to obtain research results related to the COVID-19, and hackers chose medical institutions as targets with an efficient ransomware attack approach by changing 'spray and pray' strategy to 'big-game hunting'. Companies using untact services such as telework are experiencing cyber breaches due to insufficient security settings, non-installation of security patches, and vulnerabilities in systems constituting untact services such as VPN. In response to these cyber incidents, as a case of cyber fraud countermeasures, security notices to preventing cyber fraud damage to the public was announced, and security guidelines and ransomware countermeasures were provided to organizations related to COVID-19 and medical institutions. In addition, for companies that use and provide untact services, security vulnerability finding and system development environment security inspection service were provided by Government funding programs. We also looked at the differences in the role of the government and the target of security notices between domestic and overseas response cases. Lastly, considering the development of untact services by industry in preparation for post-COVID-19, supply chain security, cloud security, development security, and IoT security were suggested as common security reinforcement measures.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.18 no.5
• /
• pp.971-980
• /
• 2023

Safety accidents and industrial accidents are constantly occurring in existing industrial sites. In addition, the probability of accidents occurring due to physical and mental fatigue of workers is increasing. Accordingly, it is required to introduce systematic management and various systems for the safety of workers. In this paper, by developing an access control system using bio-metric information at industrial sites, we develop efficient health management and access control management functions for workers. Workers are identified through face recognition for access control, and health status is determined through iris recognition. It aims to improve accuracy and develop a more efficient management system by diagnosing signs of health abnormalities through the congestion of the iris and eyes of workers. Finally, the contents of the development consist of an on-site access control system, an access control program for administrators, and a main server system that diagnoses signs of abnormal health of users.

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

• Journal of Korea Society of Industrial Information Systems
• /
• v.14 no.2
• /
• pp.32-50
• /
• 2009

Since 1990 when order communication system(OCS) was first introduced, the use of information technology in medical service has been widely accepted in order to enhance quality and customer relationship as well as to increase managerial efficiency. Medical information system is rapidly increasing and is trying to make ubiquitous healthcare environment through telemedicine system. Especially, medical profession and government have taken interest in electronic medical record (EMR) system which can digitalize and manage all medical records in hospitals. By recording patient's medical information in real time, EMR system can improve service efficiency and customer service quality including short waiting time, various utilization of clinic information, and reduced cost.