• Journal of the Korean Home Economics Association
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• v.50 no.4
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• pp.89-102
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• 2012

The purpose of this study was to demonstrate the effects of "characteristics for household management" and "attitudes toward household management" on wives' personal expenses. The data were obtained from the F-GENS Korea Panel Survey of Ochanomizu University. The responses were gathered from married people in Seoul and its surrounding metropolitan area. The sample for this study was comprised of 473 married women. ANOVA and multiple-regression models were used to analyze the data. The results are summarized below. First, 13.5 percent of the respondents have zero personal expenses. Second, the personal expenses among the wives differed depending on their annual average income levels. Also, their personal expenses differed based on their type of employment. Third, the "expenditure ratio for family" and "expenditure ratio for children" negatively affected their personal expenses. Fourth, the women who had responsibility for the management of their households had lower personal expenses than the others.

• Asian Journal of Innovation and Policy
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• v.9 no.3
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• pp.339-359
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• 2020

The policy change in the Data 3 Act is one of the issues that should be noted at a time when non-face-to-face business strategies become important after COVID-19. The Data 3 Act was implemented in South Korea on August 5, 2020, calling 'Big Data 3 Act' and 'Data Economy 3 Act,' and so personal information that was not able to identify a particular individual could be utilized without the consent of the individual. With the implementation of the Data 3 Act, it is possible to establish a fair economic ecosystem by ensuring fair access to data and various uses. In this paper, the law on the protection of personal information, which is the core of the Data 3 Act, was compared around Korea, the European Union and the United States, and the implications were derived through network analysis of keywords.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.12
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• pp.873-879
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• 2020

The importance of data as a core resource of the 4th industrial revolution is emerging, and companies illegally collect and use personal data. In the financial sector, active research is conducted to safely manage personal data and provide better services using blockchain, big data, and AI technology. In this paper, we propose a system that can safely manage personal data by using blockchain technology, which can be used without changing the existing system. The composition of this system consists of a blockchain, blockchain linkages, a service provider, and a user (i.e., an app). Blockchain can be used regardless of its type and form, and services are provided by classifying blockchains and services in the blockchain linkages. Service providers can access personal data only after requesting and receiving delegated permission from users. Existent MyData services store all data in a user's mobile phone, so information may get leaked due to jailbreaks or rooting. But in the proposed system, personal data are stored in blockchain so information leakage can be prevented. In the future, we will study ways to provide customized services using personal data stored in blockchain.

• Journal of Information Technology Services
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• v.21 no.3
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• pp.27-42
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• 2022

Open Data is an essential resource for the data industry. 'Act On Promotion Of The Provision And Use Of Public Data', enacted on July 30, 2013, mandates public institutions to manage the quality of Open Data and provide it to the public. Via such a legislation, the legal basis for the public to Open Data is prepared. Furthermore, public institutions are prohibited from developing and providing open data services that are duplicated or similar to those of the private sector, and private start-ups using open data are supported. However, as the demand for Open Data gradually increases, the cases of refusal to provide or interruption of Open Data held by public institutions are also increasing. Accordingly, the 'Open Data Mediation Committee' is established and operated so that the right to use data can be rescued through a simple dispute mediation procedure rather than complicated administrative litigation. The main issues dealt with in dispute settlement so far are usually the rights of third parties, such as open data including personal information, private information such as trade secrets, and copyrights. Plus, non-open data cannot be provided without the consent of the information subject. Rather than processing non-open data into open data through de-identification processing, positive results can be expected if consent is provided through active rights processing of the personal information subject. Not only can the Public Mydata Service be used by the information subject, but Open Data applicants will also be able to secure higher quality Open Data, which will have a positive impact on fostering the private data industry. This study derives a plan to establish a rights processing platform to enhance the usability of Open Data, including private information such as personal information, trade secrets, and copyright, which have become an issue when providing Open Data since 2014. With that, the proposals in this study are expected to serve as a stepping stone to revitalize private start-ups through the use of wide Open Data and improve public convenience through Public MyData services of information subjects.

• Journal of Information Technology Services
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• v.15 no.4
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• pp.41-62
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• 2016

In Internet of Things (IoT) era, more diverse types of information are collected and the environment of information usage, distribution, and processing is changing. Recently, there have been a growing number of cases involving breach and infringement of personal information in IoT services, for examples, including data breach incidents of Web cam service or drone and hacking cases of smart connected car or individual monitoring service. With the evolution of IoT, concerns on personal information protection has become a crucial issue and thus the risk analysis and management method of personal information should be systematically prepared. This study shows risk factors in IoT regarding possible breach of personal information and infringement of privacy. We propose "a risk analysis framework of protecting personal information in IoT environments" consisting of asset (personal information-type and sensitivity) subject to risk, threats of infringement (device, network, and server points), and social impact caused from the privacy incident. To verify this proposed framework, we conducted risk analysis of IoT services (smart communication device, connected car, smart healthcare, smart home, and smart infra) using this framework. Based on the analysis results, we identified the level of risk to personal information in IoT services and suggested measures to protect personal information and appropriately use it.

• Information Systems Review
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• v.26 no.1
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• pp.57-71
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• 2024

There are several ways to assess the value of personal data, but there is no standard for evaluating data value. In the case of medical my data utilization platform services, it was found that when the platform company received the user's consent and received data for the purpose of data utilization, an average of about 4,000 credits was paid per user as compensation. As in the previous case, the value of personal information is mainly measured based on the value of each individual, not on specific items of personal information. However, as the number and type of personal information increases, the value of personal information must be measured by type. This study focuses on measuring the value of unstructured personal information, especially images, and proposes standards for unstructured personal information. By measuring the value of images, we will be able to help platform companies set credit standards for compensation per person when providing data and support objective and reasonable pricing when selling B2B data.

• International Journal of Computer Science & Network Security
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• v.22 no.6
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• pp.310-318
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• 2022

Job offers have become more widespread and it has become easier and faster to apply for jobs through electronic recruitment platforms. In order to increase the protection of the data that is attached to the recruitment platforms. In this research, a proposed model was created through the use of hybrid encryption, which is used through the following algorithms: AES,Twofish,. This proposed model proved the effectiveness of using hybrid encryption in protecting personal data.

• The Journal of Korean Institute of Information Technology
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• v.17 no.5
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• pp.129-136
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• 2019

Many IoT devices like sensors lack screen and input devices, thus making them hard to meet the consent conditions that GDPR requires. This is acting as a legal barrier for further advancement in the business field. In this paper, we designed the process for consent of personal information collection that meets the legal conditions. In this design, user's personal data is received in an encrypted form by data collecting server first. The encrypted personal data can be decrypted after associating with user agent based on the consent procedure of the collection of personal information. During the consent procedure, user agent understands the privacy policy about personal information collection and offers the key to decrypt the data. This kind of personal information collection agreement procedure will satisfy the transparent and freely given consent requirements of GDPR. Thus, we can speculate from here that the proposed procedure will contribute to the evolution of IoT business area dealing with personal information.

• International journal of advanced smart convergence
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• v.12 no.2
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• pp.56-66
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• 2023

The goal of deep learning is to extract complex features from multidimensional data use the features to create models that connect input and output. Deep learning is a process of learning nonlinear features and functions from complex data, and the user data that is employed to train deep learning models has become the focus of privacy concerns. Companies that collect user's sensitive personal information, such as users' images and voices, own this data for indefinite period of times. Users cannot delete their personal information, and they cannot limit the purposes for which the data is used. The study has designed a deep learning method that employs privacy protection technology that uses distributed collaborative learning so that multiple participants can use neural network models collaboratively without sharing the input datasets. To prevent direct leaks of personal information, participants are not shown the training datasets during the model training process, unlike traditional deep learning so that the personal information in the data can be protected. The study used a method that can selectively share subsets via an optimization algorithm that is based on modified distributed stochastic gradient descent, and the result showed that it was possible to learn with improved learning accuracy while protecting personal information.

• Journal of the Korea Society of Computer and Information
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• v.29 no.6
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• pp.61-67
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• 2024

In this paper, we presented a strengthening plan to prevent personal information leakage incidents by securing legal compliance for personal information impact assessment and suggesting measures to strengthen privacy during personal information impact assessment. Recently, as various services based on big data have been created, efforts are being made to protect personal information, focusing on the EU's GDPR and Korea's Personal Information Protection Act. In this society, companies entrust processing of personal information to provide customized services based on the latest technology, but at this time, the problem of personal information leakage through consignees is seriously occurring. Therefore, the use of personal information by trustees.