• International Journal of Computer Science & Network Security
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• v.21 no.12
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• pp.248-256
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• 2021

Now that we're in the big data era, data has taken on a new significance as the storage capacity has exploded from trillion bytes to petabytes at breakneck pace. As the use of cloud computing expands and becomes more commonly accepted, several businesses and institutions are opting to store their requests and data there. Cloud storage's concept of a nearly infinite storage resource pool makes data storage and access scalable and readily available. The majority of them, on the other hand, favour a single cloud because of the simplicity and inexpensive storage costs it offers in the near run. Cloud-based data storage, on the other hand, has concerns such as vendor lock-in, privacy leakage and unavailability. With geographically dispersed cloud storage providers, multicloud storage can alleviate these dangers. One of the key challenges in this storage system is to arrange user data in a cost-effective and high-availability manner. A multicloud storage architecture is given in this study. Next, a multi-objective optimization problem is defined to minimise total costs and maximise data availability at the same time, which can be solved using a technique based on the non-dominated sorting genetic algorithm II (NSGA-II) and obtain a set of non-dominated solutions known as the Pareto-optimal set.. When consumers can't pick from the Pareto-optimal set directly, a method based on Principal Component Analysis (PCA) is presented to find the best answer. To sum it all up, thorough tests based on a variety of real-world cloud storage scenarios have proven that the proposed method performs as expected.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.684-687
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• 2013

빅데이터 환경은 수많은 데이터의 조합으로 가치를 발견하여 이를 활용하는 것이다. 이러한 환경의 전제조건은 데이터의 공개 및 공유 개방이 될 것이다. 하지만 데이터 공개 시 개인정보와 같은 정보가 포함되어 법적 도덕적인 문제나 공개된 정보의 범죄 활용 등 2차적인 피해가 발생할 수 있어 데이터 공개 시 개인정보에 대한 익명화가 반드시 필요하다. 하지만 익명화된 데이터는 다른 정보와 결합을 통하여 재식별되어 비익명화 될 가능성이 항상 존재한다. 따라서 본 논문에서는 데이터 공개 시 익명화된 데이터를 공개하기 전에 재식별성에 대한 위험을 평가하는 테스트 방법론을 제안한다. 제안하는 방법론은 실제 테스트를 수행하는 3가지 과정 및 테스트 레벨 설정과 익명화 시 고려해야 할 부분으로 이루어져 있다. 제안하는 방법론을 통하여 안전한 데이터 공개 환경이 조성되어 빅데이터 시대에 개인정보에 안전한 데이터 공유와 개방이 이루어질 것으로 기대한다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.1
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• pp.257-267
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• 2017

With the development of IoT (Internet of Things) technology, the application area has been diversified and the number of users using this service also has increased greatly. Real time big data generated by many IoT devices is no longer suitable for processing in a cloud computing environment. To solve this issue, fog computing is suggested which minimizes response time and makes real time processing suitable. However, security requirement for new paradigm called fog computing is not established until now. In this paper, we define models for fog computing, and the security requirements for the defined model.

• The Journal of Information Systems
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• v.28 no.1
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• pp.17-39
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• 2019

Purpose The purpose of the present study is to analyze the factors influencing the intention to use direct bank continuously from the perspective of service rather than technology. Hygiene factors including economic benefits, privacy, and unverified risks, and convenience of service as a motivator were considered as user's satisfaction. A research model integrating the relationships among user's satisfaction, its determinants, social influence, and continuous intention to use direct banks was proposed. Design/methodology/approach Structural equation modelling for validating the research model was employed. 253 valid data were collected from users of direct bank service in South Korea and China, and used to test six hypotheses. Findings User's satisfaction and social influence were determinants of continuous use intention of direct bank. Convenience of service as a motivator has a significant influence on service satisfaction, while economic benefits, privacy, and unverified risks as hygiene factors have no significant influence on the continuous intention. Managers of direct banks need to implement service differentiation strategies to gain customers' loyalty. Also they seek to find the determinants of social influence. The present study confirmed that there is a big difference between Korea and China in terms of factors affecting the continuous intention to use direct bank.

• Journal of Korean Society for Quality Management
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• v.47 no.2
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• pp.367-380
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• 2019

Purpose: The purpose of this study is (1) to empirically examine why laggards resist IT adoption in public service and (2) to investigate the relationship between resistance and negative behavioral intention. Methods: The questionnaire survey was conducted on expressway users who do not use High-pass system. The collected 232 samples were analyzed using structure equation model method with AMOS 22.0. Results: Resistance is found to be a statistically significant factor affecting intention to reject. Also, incompatibility, privacy concern, status quo inertia and relative advantage are found to be statistically significant factors affecting resistance. Conclusion: Laggards have a tendency to intend to reject IT adoption rather than to postpone. They are affected not by monetary, procedural aspect associated with purchasing or registering OBU(On Board Unit). Incompatibility and relative advantage which are inherent attributes of Hi-pass system are more influential factors than privacy concern and status quo inertia. They make ethical and emotional decision partially affected by public-social factors.

• 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.

• The Journal of the Convergence on Culture Technology
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• v.6 no.4
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• pp.553-558
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• 2020

Data has emerged as a key driver of national, corporate and individual competitiveness as a result of the entry into the data economy. The value of personal information is increasing in various fields such as customized services and social problem solving. MyData refers to a new paradigm in which individuals have the authority to manage and control their information and make active decisions on where to use and scope of personal information. MyData, which is emerging as a big topic in the data economy, is a necessary concept in an era when the value of data is important, and related laws and systems should be prepared.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.33 no.6
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• pp.1055-1065
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• 2023

Recently, various studies using deep reinforcement learning (deep RL) technology have been conducted to solve complex problems using big data collected at industrial internet of things. Deep RL uses reinforcement learning"s trial-and-error algorithms and cumulative compensation functions to generate and learn its own data and quickly explore neural network structures and parameter decisions. However, studies so far have shown that the larger the size of the learning data is, the higher are the memory usage and search time, and the lower is the accuracy. In this study, model-agnostic learning for efficient federated deep RL was utilized to solve privacy invasion by increasing robustness as 55.9% and achieve 97.8% accuracy, an improvement of 5.5% compared with the comparative optimization-based meta learning models, and to reduce the delay time by 28.9% on average.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.05a
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• pp.213-216
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• 2013

Big data, the use of privacy has been increasing to the development of wireless network infrastructure or technology development and wired Internet. By the way, Enforcement of private data preservation law the infringement accident which is still caused by despite with private data outflow occurs. The private data outflow avoids finance and to become the fire tube. Analyzes the pattern of private data from search of private data and detection process and the research which it extracts and the research is necessary in about perfection elimination of the private data which is unnecessary. From the research which it sees it researched a pattern extraction research and a complete elimination method in about private data protection and it did the pattern extraction and a complete elimination experiment of private data.