• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.6
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• pp.999-1012
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• 2020

As the utilize of personal health records increases in recent years, research on cryptographic protocol for protecting personal information of personal health records has been actively conducted. Currently, personal health records are commonly encrypted and outsourced to the cloud. However, this method is limited in verifying the integrity of personal health records, and there is a problem with poor data availability because it is essential to use it in decryption. To solve this problem, this paper proposes a verifiable cloud-based personal health record management scheme using Redactable signature scheme and zero-knowledge proof. Verifiable cloud-based personal health record management scheme can be used to verify the integrity of the original document while preserving privacy by deleting sensitive information by using Redactable signature scheme, and to verify that the redacted document has not been deleted or modified except for the deleted part of the original document by using the zero-knowledge proof. In addition, it is designed to increase the availability of data than the existing management schemes by designing to recover deleted parts only when necessary through the Redact Recovery Authority. And we propose a verifiable cloud-based personal health record management model using the proposed scheme, and analysed its efficiency by implementing the proposed scheme.

• Korean Journal of Radiology
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• v.25 no.3
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• pp.224-242
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• 2024

The emergence of Chat Generative Pre-trained Transformer (ChatGPT), a chatbot developed by OpenAI, has garnered interest in the application of generative artificial intelligence (AI) models in the medical field. This review summarizes different generative AI models and their potential applications in the field of medicine and explores the evolving landscape of Generative Adversarial Networks and diffusion models since the introduction of generative AI models. These models have made valuable contributions to the field of radiology. Furthermore, this review also explores the significance of synthetic data in addressing privacy concerns and augmenting data diversity and quality within the medical domain, in addition to emphasizing the role of inversion in the investigation of generative models and outlining an approach to replicate this process. We provide an overview of Large Language Models, such as GPTs and bidirectional encoder representations (BERTs), that focus on prominent representatives and discuss recent initiatives involving language-vision models in radiology, including innovative large language and vision assistant for biomedicine (LLaVa-Med), to illustrate their practical application. This comprehensive review offers insights into the wide-ranging applications of generative AI models in clinical research and emphasizes their transformative potential.

• Journal of Advanced Navigation Technology
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• v.28 no.4
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• pp.428-435
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• 2024

In this paper, a human activity regeneration (HAR) system based on multiple input multiple output frequency modulation continuous wave (MIMO FMCW) radar was designed and implemented. Using point cloud data from MIMO radar sensors has advantages in terms of privacy, safety, and accuracy. For the implementation of the HAR system, a customized neural network based on PointPillars and depthwise separate convolutional neural network (DS-CNN) was developed. By processing high-resolution point cloud data through a lightweight network, high accuracy and efficiency were achieved. As a result, the accuracy of 98.27% and the computational complexity of 11.27M multiply-accumulates (Macs) were achieved. In addition, the developed neural network model was implemented on Raspberry-Pi embedded system and it was confirmed that point cloud data can be processed at a speed of up to 8 fps.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.9 no.2
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• pp.374-379
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• 2005

In the most of medical institution medical information is totally stored in a database and many number of researchers and staffs of the hospital access these information anytime. This can be caused patient's privacy to be violated. Introducing a tool for security should be considered as one of the most important requirement especially in the case that today's medical information service expands into an integrated one. In this paper we review the matters of security threat on a medical information system and propose a secure medical information service model equipped on mobile device such as PDA. Also we propose a security architecture employing a digital signature mechanism to protect the personal information on the model. Proposed architecture can lead the doctor to diagnose with high responsibility, help to build a reliable medical information system. and through the signed data, we can get some useful information against medical strife.

• Journal of Information Technology Applications and Management
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• v.26 no.6
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• pp.29-46
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• 2019

This study analyzed the factors influencing the technology acceptance of the general public in the drones and ARs, one of the key technologies of the industry 4.0. The theoretical basis was the extended unified theory of acceptance and use of technology model(UTAUT2), which uses performance expectancy, effort expectancy, social influence, facilitating conditions, and hedonic motivation as factors common to both services. The price value factor was excluded considering that most ARs were free, and the perceived risk factors, including privacy, which were not in UTAUT2, were included because they are important factors for ICT technology acceptance. The hypothesis was tested by structure equation model. Social influence and hedonic motivation had a positive(+) effect on intention to use technology. On the other hand, in the case of effort expectancy, neither the AR nor the drone had a significant influence on intention to use technology. Furthermore, performance expectancy had a positive(+) effect on intention to use in AR, but no significant influence was found out in drones. On the contrary, in the case of the facilitating conditions, the influence of the drones was positive (+), but the relation of AR was not investigated. The perceived risk was tested for the negative (-) influence of use intention of AR, but no significant relationship was found out for the drones. Among the significant influencing factors, hedonic motivation was the most powerful factor in AR and drones. Theoretical and practical implications are presented based on these results.

• International Journal of Computer Science & Network Security
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• v.23 no.4
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• pp.95-102
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• 2023

The emergence of Internet of Things (IoT) into our daily lives has grown rapidly. It's been integrated to our homes, cars, and cities, increasing the intelligence of devices involved in communications. Enormous amount of data is exchanged over smart devices through the internet, which raises security concerns in regards of privacy evasion. This paper is focused on the forensics and intrusion detection on one of the most common protocols in IoT environments, especially smart home environments, which is the Message Queuing Telemetry Transport (MQTT) protocol. The paper covers general IoT infrastructure, MQTT protocol and attacks conducted on it, and multiple network forensics frameworks in smart homes. Furthermore, a machine learning model is developed and tested to detect several types of attacks in an IoT network. A forensics tool (MQTTracker) is proposed to contribute to the investigation of MQTT protocol in order to provide a safer technological future in the warmth of people's homes. The MQTT-IOT-IDS2020 dataset is used to train the machine learning model. In addition, different attack detection algorithms are compared to ensure the suitable algorithm is chosen to perform accurate classification of attacks within MQTT traffic.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.1
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• pp.1-8
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• 2024

On-device AI technology, which can operate AI models at the edge devices to support real-time processing and privacy enhancement, is attracting attention. As intelligent IoT is applied to various industries, services utilizing the on-device AI technology are increasing significantly. However, general deep learning models require a lot of computational resources for inference and learning. Therefore, various lightweighting methods such as quantization and pruning have been suggested to operate deep learning models in embedded edge devices. Among the lightweighting methods, we analyze how to lightweight and apply deep learning models to edge computing devices, focusing on pruning technology in this paper. In particular, we utilize dynamic and static pruning techniques to evaluate the inference speed, accuracy, and memory usage of a lightweight AI vision model. The content analyzed in this paper can be used for intelligent video control systems or video security systems in autonomous vehicles, where real-time processing are highly required. In addition, it is expected that the content can be used more effectively in various IoT services and industries.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.4
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• pp.1-6
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• 2016

A.I. has being developed from the technology for Big data analysis to the technology like a human being. The sensing technology of IOT will make A.I. have the more delicate sense than human's five senses. The computer resource is going to be able to support A.I. by clouding networking technology wherever and whenever. Like this A.I. is getting developed as a golden boy of the latest technologies At the same time, many experts have the anxiety and bleak outlook about A.I. Most of dystopian images of the future come out when the contemplative view is lost or it is not possible to view the phenomena objectively. Or it is because of the absence of confidence and ability to convert from the visions of technology development to the subject visions of human will. This study is not about the mass dismissal, unemployment or the end of mankind by machinery according to the development of A.I. technology and service, but more about the occurrent issue like the personal information invasion in daily life. Also the ethical and institutional models are considered to develop A.I. industry protecting the personal information.

• Journal of the Korean housing association
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• v.25 no.4
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• pp.31-38
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• 2014

The present research aimed at researching the relationships among place attachment, residential evaluations and satisfaction in a medium-sized post-communist Romanian city. Studies on post-communist cities are scarce and this research tried to fill that gap. This research is part of a government project that intended to significantly reform three medium-sized cities in the Western part of Romania and transform the urban space. Since the three of them are relatively small-sized and close spatially, the project intends to undertake massive reforms of the communications and services of the three cities. In this article, we report findings on the city of Hunedoara. A representative random sample was selected, and a total of 384 people were interviewed, with an overall reliability of the sample of 95%. The instruments used to gather the data were the Neighbourhood Perceived Environmental Quality Scale and a composite measure of place attachment was also included. The structure of each scale was checked using exploratory factor analysis. We tested alternative causal models using structural equations modelling. Our model showed a good fit to the data and explains satisfaction in the city adequately. Results show that satisfaction is directly predicted by the general evaluation of the city and by residential privacy. Residential noise and place attachment influence satisfaction indirectly. The results are discussed and some policy recommendations are formulated.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.4
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• pp.43-49
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• 2019

The wired and wireless Internet is a useful window to easily acquire various types of media data. On the other hand, the public can easily get the media data including the object to which the personal information is exposed, which is a social problem. In this paper, we propose a method to robustly detect a target object that has exposed personal information using a learning algorithm and effectively block the detected target object area. In the proposed method, only the target object containing the personal information is detected using a neural network-based learning algorithm. Then, a grid-like mosaic is created and overlapped on the target object area detected in the previous step, thereby effectively blocking the object area containing the personal information. Experimental results show that the proposed algorithm robustly detects the object area in which personal information is exposed and effectively blocks the detected area through mosaic processing. The object blocking method presented in this paper is expected to be useful in many applications related to computer vision.