• Journal of the Korea Convergence Society
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• v.9 no.10
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• pp.55-59
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• 2018

Big-data, a revolutionary technology in the era of the 4th Industrial Revolution, provides services in various fields such as health, public sector, distribution, marketing, manufacturing, etc. It is very useful technology for marketing analysis and future design through accurate and quick data analysis. It is very likely to develop further. However, the biggest problem when using Big-data is privacy and privacy. When various data are analyzed using Big-data, the tendency of each user can be analyzed, and this information may be sensitive information of an individual and may invade privacy of an individual. Therefore, in this paper, we investigate the necessary measures for Personal private information infringement that may occur when using Personal private information in Big-data environment, and propose necessary Personal private information protection technologies to contribute to protection of Personal private information and privacy.

• The Journal of Bigdata
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• v.5 no.1
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• pp.89-96
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• 2020

Deep learning using an artificial neural network has been recently researched and developed in various fields such as image recognition, big data and data analysis. However, federated learning has emerged to solve issues of data privacy invasion and problems that increase the cost and time required to learn. Federated learning presented learning techniques that would bring the benefits of distributed processing system while solving the problems of existing deep learning, but there were still problems with server-client system and motivations for providing learning data. So, we replaced the role of the server with a blockchain system in federated learning, and conducted research to solve the privacy and security problems that are associated with federated learning. In addition, we have implemented a blockchain-based system that motivates users by paying compensation for data provided by users, and requires less maintenance costs while maintaining the same accuracy as existing learning. In this paper, we present the experimental results to show the validity of the blockchain-based system, and compare the results of the existing federated learning with the blockchain-based federated learning. In addition, as a future study, we ended the thesis by presenting solutions to security problems and applicable business fields.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.670-671
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• 2013

빅데이터 환경에서 개인정보가 포함된 데이터가 공개될 경우 많은 프라이버시 문제를 야기할 수 있다. 따라서, 본 논문에서는 데이터 공개 시에 개인정보를 보호하기 위한, 공개정보 안전성 검증 체계를 제안한다. 제안하는 검증 체계는 개인정보가 포함된 공개정보에 대하여 익명화 수행을 지원하고, 익명화된 데이터에 대하여 비익명화를 수행하는 등 공개정보에 대한 안전성을 평가하고, 이를 관리 감독하는 체계이다. 안전성 검증은 공개되는 정보에 따라서 다양하게 이루어 질 수 있으며, 검증의 강도에 따라서 안전성 인증 레벨을 차등 부여한다. 제안하는 체계는 빅데이터 환경에서 데이터 공개 시 개인정보보호를 위한 최소한의 안전성 보장체계라 할 수 있으며, 제안하는 체계를 통하여 빅데이터 환경에서 개인정보에 안전한 데이터 공개 환경이 조성될 것으로 기대한다.

• Journal of the Korea Society of Computer and Information
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• v.29 no.5
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• pp.93-100
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• 2024

With the advent of the Fourth Industrial Revolution, cutting-edge technologies such as artificial intelligence, big data, the Internet of Things, and cloud computing are driving innovation across industries. These technologies are generating massive amounts of data that many companies are leveraging. However, there is a notable reluctance among users to share sensitive information due to the privacy risks associated with collecting personal data. This is particularly evident in the healthcare sector, where the collection of sensitive information such as patients' medical conditions poses significant challenges, with privacy concerns hindering data collection and analysis. This research presents a novel technique for collecting and analyzing medical data that not only preserves privacy, but also effectively extracts statistical information. This method goes beyond basic data collection by incorporating a strategy to efficiently mine statistical data while maintaining privacy. Performance evaluations using real-world data have shown that the propose technique outperforms existing methods in extracting meaningful statistical insights.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.28 no.3
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• pp.665-679
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• 2018

With the development of information and communication technology, especially wireless Internet technology and the spread of smart phones, digital data has increased. As a result, privacy issues which concerns about exposure of personal sensitive information are increasing. In this paper, we analyze the privacy vulnerability of online big data in domestic internet environment, especially focusing on portal service, and propose a measure to evaluate the possibility of privacy violation. For this purpose, we collected about 50 million user posts from the potal service contents and extracted the personal information. we find that potal service user can be identified by the extracted personal information even though the user id is partially anonymized. In addition, we proposed a risk measurement evaluation method that reflects the possibility of personal information linkage between service using partial anonymized ID and personal information exposure level.

• The Journal of Bigdata
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• v.5 no.2
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• pp.43-52
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• 2020

With the development of data technology, storing and sharing of data has increased, resulting in privacy invasion. Although de-identification technology has been introduced to solve this problem, it has been proved many times that identifying individuals using de-identified data is possible. Even if it cannot be completely safe, sufficient de-identification is necessary. But current laws and regulations do not quantitatively specify the degree of how much de-identification should be performed. In this paper, we propose an appropriate de-identification criterion considering the time required for re-identification. We focused on the case of using the k-anonymity model among various privacy models. We analyzed the time taken to re-identify data according to the change in the k value. We used a re-identification method based on linkability. As a result of the analysis, we determined which k value is appropriate. If the generalized model can be developed by results of this paper, the model can be used to define the appropriate level of de-identification in various laws and regulations.

• Journal of Digital Contents Society
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• v.18 no.8
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• pp.1671-1676
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• 2017

In theses days, data has been explosively generated in diverse industrial areas. Accordingly, many industries want to collect and analyze these data to improve their products or services. However, collecting user data can lead to significant personal information leakage. Local differential privacy (LDP) proposed by Google is the state-of-the-art approach that is used to protect individual privacy in the process of data collection. LDP guarantees that the privacy of the user is protected by perturbing the original data at the user's side, but a data collector is still able to obtain population statistics from collected user data. However, the prevention of leakage of personal information through such data perturbation mechanism may cause the significant reduction in the data utilization. Therefore, the degree of data perturbation in LDP should be set properly depending on the data collection and analysis purposes. Thus, in this paper, we develop the simulation tool which aims to help the data collector to properly chose the degree of data perturbation in LDP by providing her/him visualized simulated results with various parameter configurations.

• Informatization Policy
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• v.23 no.1
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• pp.3-19
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• 2016

The purpose of the study is to analyze the existing literature and to suggest future research directions in the big data security area. This study identifies 62 research articles and analyses their publication year, publication media, general research approach, specific research method, and research topic. According to the results of the analyses, big data security research is at its intial stage in which non-empirical studies and research dealing with technical issues are dominant. From the research topic perspective, the area demonstrates the signs of initial research stage in which proportion of the macro studies dealing with overall issues is far higher than the micro ones covering specific implementation methods and sectoral issues. A few promising topics for future research include overarching framework on big data security, big data security methods for different industries, and government policies on big data security. Currently, the big data security area does not have sufficient research results. In the future, studies covering various topics in big data security from multiple perspectives are anticipated.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.30 no.4
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• pp.679-691
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• 2020

In the future, 5G technology will become the core infrastructure driving the 4th industrial era. For intelligent super-convergence service, it will be necessary to collect various personal information such as location data. If a person's high-precision location information is exposed by a malicious person, it can be a serious privacy risk. In the past, various approaches have been researched through encryption and obfuscation to protect location information privacy. In this paper, we proposed a new technique that enables statistical query and data analysis without exposing location information. The proposed method does not allow the original to be re-identified through polynomial-based transform processing. In addition, since the quality of the original data is not compromised, the usability of positioning big data can be maximized.

• Journal of Digital Convergence
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• v.14 no.2
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• pp.65-72
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• 2016

This study built the theoretical frameworks for empirical analysis based on the analysis of the relationship among the concepts of risk of information privacy, the experience of information privacy, the policy of information privacy and information control via the provision intention studies. Also, in order to analyze the relationship among the factors such as the risk of information privacy, intention to offer the personal information, this study investigated the concepts of information privacy and studies related with the privacy, established a research model about the information privacy. Followings are the results of this study: First, the information privacy risk, information privacy experience, information privacy policy, and information control have positive effects upon the information privacy concern. Second, the information privacy concern has the negative effects upon the provision intention of personal information.