• International Journal of Internet, Broadcasting and Communication
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• v.15 no.4
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• pp.261-269
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• 2023

We propose to design a Holochain-based security and privacy protection system for resource-constrained IoT healthcare systems. Through analysis and performance evaluation, the proposed system confirmed that these characteristics operate effectively in the IoT healthcare environment. The system proposed in this paper consists of four main layers aimed at secure collection, transmission, storage, and processing of important medical data in IoT healthcare environments. The first PERCEPTION layer consists of various IoT devices, such as wearable devices, sensors, and other medical devices. These devices collect patient health data and pass it on to the network layer. The second network connectivity layer assigns an IP address to the collected data and ensures that the data is transmitted reliably over the network. Transmission takes place via standardized protocols, which ensures data reliability and availability. The third distributed cloud layer is a distributed data storage based on Holochain that stores important medical information collected from resource-limited IoT devices. This layer manages data integrity and access control, and allows users to share data securely. Finally, the fourth application layer provides useful information and services to end users, patients and healthcare professionals. The structuring and presentation of data and interaction between applications are managed at this layer. This structure aims to provide security, privacy, and resource efficiency suitable for IoT healthcare systems, in contrast to traditional centralized or blockchain-based systems. We design and propose a Holochain-based security and privacy protection system through a better IoT healthcare system.

• Journal of Digital Convergence
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• v.13 no.4
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• pp.227-233
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• 2015

The recent health care is growing rapidly want to receive offers users a variety of medical services, can be exploited easily exposed to a third party information on the role of the patient's hospital staff (doctors, nurses, pharmacists, etc.) depending on the patient clearly may have to be classified. In this paper, in order to ensure safe use by third parties in the health care environment, classify the attributes of patient information and patient privacy protection technique using hierarchical multi-property rights proposed to classify information according to the role of patient hospital officials The. Hospital patients and to prevent the proposed method is represented by a mathematical model, the information (the data consumer, time, sensor, an object, duty, and the delegation circumstances, and so on) the privacy attribute of a patient from being exploited illegally patient information from a third party the prevention of the leakage of the privacy information of the patient in synchronization with the attribute information between the parties.

• 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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.1
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• pp.437-454
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• 2020

According to the privacy regulations of the health insurance portability and accountability act (HIPAA), patients' control over electronic health data is one of the major concern issues. Currently, remote access authorization is considered as the best solution to guarantee the patients' control over their health data. In this paper, a new biometric-based key management scheme is proposed to facilitate remote access authorization anytime and anywhere. First, patients and doctors can use their biometric information to verify the authenticity of communication partners through real-time video communication technology. Second, a safety channel is provided in delivering their access authorization and secret data between patient and doctor. In the designed scheme, the user's public key is authenticated by the corresponding biometric information without the help of public key infrastructure (PKI). Therefore, our proposed scheme does not have the costs of certificate storage, certificate delivery, and certificate revocation. In addition, the implementation time of our proposed system can be significantly reduced.

• Journal of Korean society of Dental Hygiene
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• v.18 no.4
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• pp.561-570
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• 2018

Objectives: This study explored the perception and practice of privacy protection of some dental hygiene students. Methods: On the basis of survey data from 126 respondents, the correlation between the perception and the practice was analyzed. Also the multiple regression analysis was performed on the variables that affect the practice. Cronbach's ${\alpha}$ of the questionnaire was more than 0.6. The items were scored on 5 points scale or true-false type. Results: The perception of privacy protection was 3.23 points, the law is 0.88 points, and the practice is 3.47 points. The educated students were more perceive than those who did not(p<0.05). The higher the perception, the higher the practice(r=0.230, p<0.01). The practice was influenced by the perception(p<0.05). Conclusions: Dental hygiene students should be educated to perceive and protect the personal and medical information of a patient. Also, an educational institutions need a efforts to protect personal information.

• The Journal of the Convergence on Culture Technology
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• v.9 no.3
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• pp.647-654
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• 2023

Deep Learning is a useful method for classifying and recognizing complex data such as images and text, and the accuracy of the deep learning method is the basis for making artificial intelligence-based services on the Internet useful. However, the vast amount of user da vita used for training in deep learning has led to privacy violation problems, and it is worried that companies that have collected personal and sensitive data of users, such as photographs and voices, own the data indefinitely. Users cannot delete their data and cannot limit the purpose of use. For example, data owners such as medical institutions that want to apply deep learning technology to patients' medical records cannot share patient data because of privacy and confidentiality issues, making it difficult to benefit from deep learning technology. In this paper, we have designed a privacy preservation technique-applied deep learning technique that allows multiple workers to use a neural network model jointly, without sharing input datasets, in multi-party system. We proposed a method that can selectively share small subsets using an optimization algorithm based on modified stochastic gradient descent, confirming that it could facilitate training with increased learning accuracy while protecting private information.

• Journal of the Institute of Electronics and Information Engineers
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• v.51 no.10
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• pp.128-136
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• 2014

Recently, although exponentially growing the quantity of information that have been used and shared on internet networks, the patient information of each medical center have not been used and shared among medical centers due to the protection of patients privacy and the different database schema. To address this problem, we have studied the data structure of the patient information, the standard of medical information for patients we propose a patient information sharing system design that each medical center is able to use and share the patient information among medical centers in spite of different patient information systems with protecting patients privacy.

• Journal of the Korea Society of Computer and Information
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• v.25 no.10
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• pp.59-67
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• 2020

With the development of wearable devices, individuals' health status can be checked in real time and risks can be predicted. For example, an application has been developed to detect an emergency situation of a patient with heart disease and contact a guardian through analysis of health data such as heart rate and electrocardiogram. However, health data is seriously damaging when it is leaked as it relates to life. Therefore, a method to protect personal information is essential in collecting health data, and this study proposes a method of collecting data while protecting the personal information of the data owner through a LDP(Local Differential Privacy). The previous study introduced a technique of transmitting feature point data rather than all data to a data collector as an algorithm for searching for fixed k feature points. Next, this study will explain how to improve the performance by up to 75% using an algorithm that finds the optimal number of feature points k.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.20 no.6
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• pp.251-260
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• 2010

Through the convergence of medical services and the IT technique, the patient's personal health information computerization has been rapidly spread with propagation of electronic medical record(EHR). In addition, by entering u-health, the demand of the secondary use for public health, medical research, and medical service using electronic patient health care records are increasing. The personal health information secondary uses for the development of academic medical area and service, are very good thing. But, carelessly to use personal health information, the patient privacy would be damaged. However, there are not yet systematic studies about secondary use of personal health information. Therefore, in this paper, we analyze the difference of the internal and external bill for personal medical data secondary use and propose the direction of the medical service development and preservation of the individual's privacy.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.3
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• pp.958-979
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• 2023

Due to laws, regulations, privacy, etc., between 70-90 percent of providers do not share medical data, forming a "data island". It is essential to collaborate across multiple institutions without sharing patient data. Most existing methods adopt distributed learning and centralized federal architecture to solve this problem, but there are problems of resource heterogeneity and data heterogeneity in the practical application process. This paper proposes a collaborative deep learning modelling method based on the blockchain network. The training process uses encryption parameters to replace the original remote source data transmission to protect privacy. Hyperledger Fabric blockchain is adopted to realize that the parties are not restricted by the third-party authoritative verification end. To a certain extent, the distrust and single point of failure caused by the centralized system are avoided. The aggregation algorithm uses the FedProx algorithm to solve the problem of device heterogeneity and data heterogeneity. The experiments show that the maximum improvement of segmentation accuracy in the collaborative training mode proposed in this paper is 11.179% compared to local training. In the sequential training mode, the average accuracy improvement is greater than 7%. In the parallel training mode, the average accuracy improvement is greater than 8%. The experimental results show that the model proposed in this paper can solve the current problem of centralized modelling of multicenter data. In particular, it provides ideas to solve privacy protection and break "data silos", and protects all data.