• Knowledge Management Research
• /
• v.18 no.3
• /
• pp.63-80
• /
• 2017

Big Data environment is established by accumulating vast amounts of data as users continuously share and provide personal information in online environment. Accordingly, the more data is accumulated in online environment, the more data is accessible easily by third parties without users' permissions compared to the past. By utilizing strategies based on data-driven, firms recently make it possible to predict customers' preferences and consuming propensity relatively exactly. This Big Data environment, on the other hand, establishes 'Dataveillance' which means anybody can watch or control users' behaviors by using data itself which is stored online. Main objective of this study is to identify the relationship between Dataveillance and users' online privacy protection behaviors. To achieve it, we first investigate perceived online service efficiency; loss of control on privacy; offline surveillance; necessity of regulation influences on users' perceived threats which is generated by Dataveillance.

• Journal of Intelligence and Information Systems
• /
• v.19 no.1
• /
• pp.125-139
• /
• 2013

Big data refers to the data that cannot be processes with conventional contemporary data technologies. As smart devices and social network services produces vast amount of data, big data attracts much attention from researchers. There are strong demands form governments and industries for bib data as it can create new values by drawing business insights from data. Since various new technologies to process big data introduced, academic communities also show much interest to the big data domain. A notable advance related to the big data technology has been in various fields. Big data technology makes it possible to access, collect, and save individual's personal data. These technologies enable the analysis of huge amounts of data with lower cost and less time, which is impossible to achieve with traditional methods. It even detects personal information that people do not want to open. Therefore, people using information technology such as the Internet or online services have some level of privacy concerns, and such feelings can hinder continued use of information systems. For example, SNS offers various benefits, but users are sometimes highly exposed to privacy intrusions because they write too much personal information on it. Even though users post their personal information on the Internet by themselves, the data sometimes is not under control of the users. Once the private data is posed on the Internet, it can be transferred to anywhere by a few clicks, and can be abused to create fake identity. In this way, privacy intrusion happens. This study aims to investigate how perceived personal information overload in SNS affects user's risk perception and information privacy concerns. Also, it examines the relationship between the concerns and user resistance behavior. A survey approach and structural equation modeling method are employed for data collection and analysis. This study contributes meaningful insights for academic researchers and policy makers who are planning to develop guidelines for privacy protection. The study shows that information overload on the social network services can bring the significant increase of users' perceived level of privacy risks. In turn, the perceived privacy risks leads to the increased level of privacy concerns. IF privacy concerns increase, it can affect users to from a negative or resistant attitude toward system use. The resistance attitude may lead users to discontinue the use of social network services. Furthermore, information overload is mediated by perceived risks to affect privacy concerns rather than has direct influence on perceived risk. It implies that resistance to the system use can be diminished by reducing perceived risks of users. Given that users' resistant behavior become salient when they have high privacy concerns, the measures to alleviate users' privacy concerns should be conceived. This study makes academic contribution of integrating traditional information overload theory and user resistance theory to investigate perceived privacy concerns in current IS contexts. There is little big data research which examined the technology with empirical and behavioral approach, as the research topic has just emerged. It also makes practical contributions. Information overload connects to the increased level of perceived privacy risks, and discontinued use of the information system. To keep users from departing the system, organizations should develop a system in which private data is controlled and managed with ease. This study suggests that actions to lower the level of perceived risks and privacy concerns should be taken for information systems continuance.

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

• Journal of Convergence for Information Technology
• /
• v.10 no.4
• /
• pp.18-24
• /
• 2020

Recently, research on Society 5.0 has been actively carried out in Japan. The Society 5.0 is used in various areas using IoT sensors. This paper proposes a privacy approach model of multiple approaches to IoT users based on Society 5.0. The proposed model used multiple methods of synchronizing important information of IoT devices with one another in the virtual environment. The proposed model improved the efficiency of IoT information by accumulating the weight of IoT information on a probability-based basis. Further, it improves the accuracy of IoT information by segmenting it so that attribute information is linked to IoT information. As a result of the performance evaluation, the efficiency of IoT devices has improved by an average of 5.6 percent, depending on the number of IoT devices and the number of IoT hub devices. Accuracy has improved by an average of 15.9% depending on information collection and processing.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.32 no.1
• /
• pp.141-154
• /
• 2022

In the data economy era, various policies are being implemented to create an active data distribution environment. In South Korea, the formation of a big data distribution platform and data trading began with the launch of the Financial Data Exchange under public data governance. In the case of major advanced countries in the data field, they have built a data distribution environment based on the data broker industry for decades and have strengthened national data competitiveness through added values generated from the industry. However, behind the active data distribution through data brokers, there are numerous information security issues, which have resulted in various privacy issues and national security threats. These problems can occur sufficiently in the process of domestic financial data exchange. In our study, we analyzed various information security issues of data trading caused by data brokers and derived information security requirements to be considered when trading data. We verified whether information security requirements are well reflected in the information security policy for each transaction stage of the domestic financial data exchange. Based on the verification, measurements to strengthen information security for financial data exchange are presented in our paper.

• Journal of Korea Society of Digital Industry and Information Management
• /
• v.12 no.3
• /
• pp.1-9
• /
• 2016

The current meaning of "Big Data" refers to all the techniques for value eduction and actionable analytics as well management tools. Particularly, with the advances of wireless sensor networks, they yield diverse patterns of digital records. The records are mostly semi-structured and unstructured data which are usually beyond of capabilities of the management tools. Such data are rapidly growing due to their complex data structures. The complex type effectively supports data exchangeability and heterogeneity and that is the main reason their volumes are getting bigger in the sensor networks. However, there are many errors and problems in applications because the managing solutions for the complex data model are rarely presented in current big data environments. To solve such problems and show our differentiation, we aim to provide the solution of actionable analytics and semantic reusability in the sensor web based streaming big data with new data structure, and to empower the competitiveness.

• Electronics and Telecommunications Trends
• /
• v.32 no.1
• /
• pp.35-45
• /
• 2017

모든 사회 생활 및 경제 활동이 인터넷을 통해 이루어지며 IoT, 빅데이터, 모바일, 클라우드가 연결되는 초연결 시대를 대비하기 위하여 신뢰성이 담보된 차세대 네트워킹 기술이 요구되고 있다. 신뢰성 있는 IP네트워킹 기술은 속도와 기능 및 성능의 고도화를 넘어서 새로운 환경에 적응이 가능한 유연한 구조와 다양한 상황에 대응하는 지능적 처리 방식으로 혁신되어야 하며 안전한 초연결 서비스를 위해서 프라이버시와 보안이 핵심으로 제공되어야 한다. 본고에서는 최근의 IP 네트워크 기술동향을 살펴보고 이를 기반으로 초연결 세상을 구현하기 위한 중심에 있는 신뢰 네트워크에 대한 개념을 새로이 정의하며, 필수적인 요구사항들과 기능들을 분석하고 이를 기반으로 적용 가능한 다양한 응용 분야를 도출하며, 지속적인 향후 연구방향에 대해서도 살펴보고자 한다.

• Journal of the Korea Institute of Information Security & Cryptology
• /
• v.29 no.5
• /
• pp.1117-1132
• /
• 2019

Recently, IT technology such as internet, mobile, and IOT has rapidly developed, making it easy to collect data necessary for business, and the collected data is analyzed as a new method of big data analysis and used appropriately for business. In this way, data collection and analysis becomes easy. In such data, personal information including an identifier such as a sensor id, a device number, IP address, or the like may be collected. However, if systematic management is not accompanied by collecting and disposing of large-scale data, violation of relevant laws such as "Personal Data Protection Act". Furthermore, data quality problems can also occur and make incorrect decisions. In this paper, we propose a new data governance maturity model(DGMM) that can identify the personal data contained in the data collected by companies, use it appropriately for the business, protect it, and secure quality. And we also propose a over all implementation process for DG Program.

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

• Journal of Digital Convergence
• /
• v.16 no.11
• /
• pp.137-147
• /
• 2018

To prepare for and implement policies for the Fourth Industrial Revolution, which is characterized by convergence, super-connectivity, and AI, this study summarized the effects and characteristics of individual technologies on our society and discussed the issues with humanity and social science perspectives. As a result, in terms of AI technology, the issues of job losses, project-type works, basic income and robot taxes, accountability of AI, and algorithm inequality were dealt with. Security, cyber hacking and privacy infringement issues were highlighted in big-data technology. In the part of block-chain and bioengineering, the society of decentralization, the concentration, digital divide, and ethical issues were discussed. On-demand economic aspects highlighted the problems of civil ethics and human commercialization. Lastly, the development of VR is discussed including side effects such as cyber-syndrom, avoidance of reality, and so on.