• The Journal of Korean Institute of Information Technology
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• v.17 no.6
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• pp.115-122
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• 2019

CCTV(Closed-Circuit Television) is increasingly exposed to CCTV per person as the number of installations increases every year for accident prevention and facility safety. The intelligent video surveillance system technology is attracting attention to the privacy protection of exposed subjects. The intelligent video surveillance system performs a process for the privacy protection so as to perform the action type of the subject and the judgment of the situation in the simple identification of the photographed image data, or to prevent the information, from which the information of the photographed subject is exposed. The proposed technique is applied to the video surveillance system and converts the original image information taken from the video surveillance system into similar image information so that the original image information is not leaked to the outside. In this paper, we propose an image conversion mechanism that inserts a virtual face image that approximates a preset similarity.

• The Journal of Korean Institute of Information Technology
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• v.17 no.5
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• pp.105-116
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• 2019

Most of the public and private buildings in Korea are installing CCTV for crime prevention and follow-up action, insider security, facility safety, and fire prevention, and the number of installations is increasing each year. In the questionnaire conducted on the increasing CCTV, many reactions were positive in terms of the prevention of crime that could occur due to the installation, rather than negative views such as privacy violation caused by CCTV shooting. However, CCTV poses a lot of privacy risks, and when the image data is collected using the cloud, the personal information of the subject can be leaked. InseCam relayed the CCTV surveillance video of each country in real time, including the front camera of the notebook computer, which caused a big issue. In this paper, we introduce a system to prevent leakage of private information and enhance the security of the cloud system by processing the privacy technique on image information about a subject photographed through CCTV.

• Journal of the Korea Convergence Society
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• v.13 no.2
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• pp.201-214
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• 2022

This study examined the relationship between young adults' personality traits and mobile shopping intention, through mediation effects of privacy concern and perceived value of purchase based oh the mobile devices. For purposes of the study, data were collected from a convenience sample of 204 undergraduate students who participated in introduction to psychology and marketing at a large university in South Korea. To test the research model, this study used a path analysis with a maximum likelihood method and a regression analysis with a parallel mediation using bootstrapping procedures. The study results indicated that privacy concern can mediate the effects of neuroticism and agreeableness on mobile shopping intentions. Also, the relationship between openness and mobile shopping intention was mediated by the perceived value of mobile commerce. From these results, it suggests that mobile marketers should consider personality traits of young adults for customizing the personalized mobile marketing strategies.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.6 no.9
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• pp.267-276
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• 2016

There has been active discussions on informational view among various privacy perspectives because of Internet usage. Social science research has largely discussed determinants of information privacy concerns (IPC) and its behavioral outcomes. However, the focus of studies was limited to company's practices and existing studies have not reported consistent results. In addition, the relationship between demographic characteristics of user and IPC has not been discussed enough. In this study, we re-investigate the relationship between users' demographic characteristics and IPC with data from a large sample provided by the Korea Information Society Development Institute (KISDI). The results showed that educational background and income level have effects on IPC in line with existing studies. But the effects of gender and age had different impact on IPC compared to previous studies. This study has implications in enabling generalization of empirical findings and confirming the results of previous studies.

• Journal of Internet Computing and Services
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• v.17 no.6
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• pp.81-91
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• 2016

VANET is one of the most developed technologies many people have considered a technology for the next generation. It basically utilizes the wireless technology and it can be used for measuring the speed of the vehicle, the location and even traffic control. With sharing those information, VANET can offer Cooperative ITS which can make a solution for a variety of traffic issues. In this way, safety for drivers, efficiency and mobility can be increased with VANET but data between vehicles or between vehicle and infrastructure are included with private information. Therefore alternatives are necessary to secure privacy. If there is no alternative for privacy, it can not only cause some problems about identification information but also it allows attackers to get location tracking and makes a target. Besides, people's lives or property can be dangerous because of sending wrong information or forgery. In addition to this, it is possible to be information stealing by attacker's impersonation or private information exposure through eavesdropping in communication environment. Therefore, in this paper we propose Privacy Assurance Architecture for VANET to ensure privacy from these threats.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.19 no.4
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• pp.125-135
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• 2009

Logs from various security system can reveal the attack trials for accessing private data without authorization. The logs can be a kind of confidence deriving factors that a certain IP address is involved in the trial. This paper presents a rule-based expert system for derivation of privacy violation confidence using various security systems. Generally, security manager analyzes and synthesizes the log information from various security systems about a certain IP address to find the relevance with privacy violation cases. The security managers' knowledge handling various log information can be transformed into rules for automation of the log analysis and synthesis. Especially, the coverage of log analysis for personal information leakage is not too broad when we compare with the analysis of various intrusion trials. Thus, the number of rules that we should author is relatively small. In this paper, we have derived correlation among logs from IDS, Firewall and Webserver in the view point of privacy protection and implemented a rule-based expert system based on the derived correlation. Consequently, we defined a method for calculating the score which represents the relevance between IP address and privacy violation. The UI(User Interface) expert system has a capability of managing the rule set such as insertion, deletion and update.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.2
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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.

• The Journal of the Convergence on Culture Technology
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• v.10 no.1
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• pp.435-441
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• 2024

In order for the large amount of collected data sets to be used as deep learning training data, sensitive personal information such as resident registration number and disease information must be changed or encrypted to prevent it from being exposed to hackers, and the data must be reconstructed to match the structure of the built deep learning model. Currently, these tasks are performed manually by experts, which takes a lot of time and money. To solve these problems, this paper proposes a technique that can automatically perform data processing tasks to protect personal information during the deep learning process. In the proposed technique, privacy protection tasks are performed based on data generalization and data reconstruction tasks are performed using circular queues. To verify the validity of the proposed technique, it was directly implemented using C language. As a result of the verification, it was confirmed that data generalization was performed normally and data reconstruction suitable for the deep learning model was performed properly.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.12
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• pp.291-306
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• 2020

Nowadays, Data-Network-AI (DNA)-based intelligent services and applications have become a reality to provide a new dimension of services that improve the quality of life and productivity of businesses. Artificial intelligence (AI) can enhance the value of IoT data (data collected by IoT devices). The internet of things (IoT) promotes the learning and intelligence capability of AI. To extract insights from massive volume IoT data in real-time using deep learning, processing capability needs to happen in the IoT end devices where data is generated. However, deep learning requires a significant number of computational resources that may not be available at the IoT end devices. Such problems have been addressed by transporting bulks of data from the IoT end devices to the cloud datacenters for processing. But transferring IoT big data to the cloud incurs prohibitively high transmission delay and privacy issues which are a major concern. Edge computing, where distributed computing nodes are placed close to the IoT end devices, is a viable solution to meet the high computation and low-latency requirements and to preserve the privacy of users. This paper provides a comprehensive review of the current state of leveraging deep learning within edge computing to unleash the potential of IoT big data generated from IoT end devices. We believe that the revision will have a contribution to the development of DNA-based intelligent services and applications. It describes the different distributed training and inference architectures of deep learning models across multiple nodes of the edge computing platform. It also provides the different privacy-preserving approaches of deep learning on the edge computing environment and the various application domains where deep learning on the network edge can be useful. Finally, it discusses open issues and challenges leveraging deep learning within edge computing.

• Journal of Platform Technology
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• v.11 no.3
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• pp.56-67
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• 2023

Recently, de-identification of personal information, which has been a long-cherished desire of the data-based industry, was revised and specified in August 2020. It became the foundation for activating data called crude oil[2] in the fourth industrial era in the industrial field. However, some people are concerned about the infringement of the basic rights of the data subject[3]. Accordingly, a development study was conducted on the Batch De-Identification Tool, a personal information de-identification automation tool. In this study, first, we developed an image labeling tool to label human faces (eyes, nose, mouth) and car license plates of various resolutions to build data for training. Second, an object recognition model was trained to run the object recognition module to perform de-identification of personal information. The automated personal information de-identification tool developed as a result of this research shows the possibility of proactively eliminating privacy violations through online services. These results suggest possibilities for data-based industries to maximize the value of data while balancing privacy and utilization.