• International Journal of Computer Science & Network Security
• /
• v.22 no.4
• /
• pp.340-350
• /
• 2022

The big data term refers to the great volume of data and complicated data structure with difficulties in collecting, storing, processing, and analyzing these data. Big data analytics refers to the operation of disclosing hidden patterns through big data. This information and data set cloud to be useful and provide advanced services. However, analyzing and processing this information could cause revealing and disclosing some sensitive and personal information when the information is contained in applications that are correlated to users such as location-based services, but concerns are diminished if the applications are correlated to general information such as scientific results. In this work, a survey has been done over security and privacy challenges and approaches in big data. The challenges included here are in each of the following areas: privacy, access control, encryption, and authentication in big data. Likewise, the approaches presented here are privacy-preserving approaches in big data, access control approaches in big data, encryption approaches in big data, and authentication approaches in big data.

• International Journal of Computer Science & Network Security
• /
• v.21 no.7
• /
• pp.43-55
• /
• 2021

The amount of Big Data generated from multiple sources is continuously increasing. Traditional storage methods lack the capacity for such massive amounts of data. Consequently, most organizations have shifted to the use of cloud storage as an alternative option to store Big Data. Despite the significant developments in cloud storage, it still faces many challenges, such as privacy and security concerns. This paper discusses Big Data, its challenges, and different classifications of security and privacy challenges. Furthermore, it proposes a new classification of Big Data security and privacy challenges and offers some perspectives to provide solutions to these challenges.

• Journal of Korea Multimedia Society
• /
• v.20 no.11
• /
• pp.1785-1792
• /
• 2017

Generally, big data contains sensitive information about individuals, and thus directly releasing it for public use may violate existing privacy requirements. Therefore, privacy-preserving data publishing (PPDP) has been actively researched to share big data containing personal information for public use, while protecting the privacy of individuals with minimal data modification. Recently, with increasing demand for big data sharing in various area, there is also a growing interest in the development of software which supports a privacy-preserving data publishing. Thus, in this paper, we develops the system which aims to effectively and efficiently support privacy-preserving data publishing. In particular, the system developed in this paper enables data owners to select the appropriate anonymization level by providing them the information loss matrix. Furthermore, the developed system is able to achieve a high performance in data anonymization by using distributed Hadoop clusters.

• Journal of Digital Convergence
• /
• v.11 no.11
• /
• pp.143-153
• /
• 2013

Big data is expected to bring forth enormous public good as well as economic opportunity. However there is ongoing concern about privacy not only from public authorities but also from private enterprises. Big data is suspected to aggravate the existing privacy battle ground by introducing new types of privacy risks such as privacy risk of behavioral pattern. On the other hand, big data is asserted to become a new way to by-pass tradition behavioral tracking such as cookies, DPIs, finger printing${\cdots}$ and etc. For it is not based on a targeted person. This paper is to find out if big data could contribute to catching out behavioral patterns of consumers without threatening or damaging their privacy. The difference between traditional behavioral tracking and big data analysis from the perspective of privacy will be discerned.

• Journal of Digital Convergence
• /
• v.12 no.12
• /
• pp.87-92
• /
• 2014

We have noted a possibility of big data as a solution of social problem and pending issue. At the same time big data has a problem of privacy. Big data and privacy were in conflict. In this paper we pointed out that issue and propose a planning of big data based on privacy using case study of advanced country.

• Journal of the Korea Society of Computer and Information
• /
• v.23 no.11
• /
• pp.17-24
• /
• 2018

Today, we are living in the era of data and information. With the advent of Internet of Things (IoT), the popularity of social networking sites, and the development of mobile devices, a large amount of data is being produced in diverse areas. The collection of such data generated in various area is called big data. As the importance of big data grows, there has been a growing need to share big data containing information regarding an individual entity. As big data contains sensitive information about individuals, directly releasing it for public use may violate existing privacy requirements. Thus, privacy-preserving data publishing (PPDP) has been actively studied to share big data containing personal information for public use, while preserving the privacy of the individual. K-anonymity, which is the most popular method in the area of PPDP, transforms each record in a table such that at least k records have the same values for the given quasi-identifier attributes, and thus each record is indistinguishable from other records in the same class. As the size of big data continuously getting larger, there is a growing demand for the method which can efficiently anonymize vast amount of dta. Thus, in this paper, we develop an efficient k-anonymity method by using Spark distributed framework. Experimental results show that, through the developed method, significant gains in processing time can be achieved.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.14 no.1
• /
• pp.189-193
• /
• 2014

SNS (Social Network Service) has evolved to life-friendly service with the combination of local services. Unlike exsiting mobile services, life-friendly service is expected to be personalized with gathering of local information, location information and social network service information. In the process of gathering various kinds of information, Big-data technology and Cloud technology is needed. The effective algorithem has researched for this already, however the privacy protection model hasn't researched enough in life-friendly service or big-data using circumstance. In this paper, the privacy issue is dealt with in terms of 'Structure hole', and the privacy issue comes from big-data technology of life-friendly service.

• 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 Convergence Society
• /
• v.9 no.10
• /
• pp.55-59
• /
• 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.

• Informatization Policy
• /
• v.24 no.1
• /
• pp.79-99
• /
• 2017

Despite the early success story, the pan-industry diffusion of big data has been slow mostly due to lack of confidence of the value creation and privacy-related concerns. The problem leads us to the need to a stakeholder analysis on the adoption process of big data. The present study combines technology acceptance model, task-technology fit theory, and privacy calculus theory to integrate the positive and negative factors on the big data adoption. The empirical analysis was performed based on the survey from the current and potential big data users. Results revealed perceived usefulness, task-technology fit, and privacy concern are significant antecedents to the intention to use big data. Furthermore, there are significant differences in the perceptions of each constructs among groups divided by the types of big data use, with several exceptions. And the control effect was found in the magnitude of the relation between independent variables and dependent variable. The theoretical and politic implications of the analysis are discussed as to the promotion of big data industry.