• International Journal of Internet, Broadcasting and Communication
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• v.12 no.1
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• pp.137-143
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• 2020

With the development of information and communication technology, various data have appeared and are being distributed. The use of various data has contributed to the enrichment and convenience of our lives. Data in the public areas is also growing in volume and being actively used. Public data in the field of education are also used in various ways. As the distribution and use of public data has increased, advantages and disadvantages have started to emerge. Among the various disadvantages, the privacy problem is a representative one. In this study, we deal with the privacy issues of public data in education. First, we introduce the privacy issues of public data in the education field and suggest various solutions. The various solutions include the expansion of privacy education opportunities, the need for a new privacy protection model, the provision of a training opportunity for privacy protection for teachers and administrators, and the development of a real-time privacy infringement diagnosis tool.

• Journal of Internet Computing and Services
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• v.7 no.1
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• pp.1-16
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• 2006

The vulnerability of privacy in the Identity Management System (IMS) is the most pressing concern of ordinary users. Uncertainty about privacy keeps many users away from utilization of IMS. Therefore, this paper proposes an access-control oriented privacy model for IMS. The proposed model protects privacy using access control techniques with privacy policies in a single circle of trust. We address characteristics of the components of for the proposed model and describe access control procedures. After that, we show the architecture of privacy enforcement and XML-based schema for privacy policies.

• Knowledge Management Research
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• v.16 no.3
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• pp.23-40
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• 2015

Despite much interest and investment in the Internet of Things (IoT) which expand the Internet to a ubiquitous network including objects in the physical world, there is growing concerns of privacy protections. Because the risk of privacy invasion is higher in IoT environments than ever before, privacy need to be a key issue in the diffusion of IoT. Considering that the privacy concern is a critical barrier for user to adopt information technologies, it is important to investigate users' privacy concerns related to IoT applications. From the triad perspective (i.e., risk on technology, risk on service provider, and trust on legislation), this study aims to examine users' privacy concerns in the context of activity trackers.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.5
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• pp.1272-1290
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• 2013

Privacy-preserving collaborative filtering schemes are becoming increasingly popular because they handle the information overload problem without jeopardizing privacy. However, they may be susceptible to shilling or profile injection attacks, similar to traditional recommender systems without privacy measures. Although researchers have proposed various privacy-preserving recommendation frameworks, it has not been shown that such schemes are resistant to profile injection attacks. In this study, we investigate two memory-based privacy-preserving collaborative filtering algorithms and analyze their robustness against several shilling attack strategies. We first design and apply formerly proposed shilling attack techniques to privately collected databases. We analyze their effectiveness in manipulating predicted recommendations by experimenting on real data-based benchmark data sets. We show that it is still possible to manipulate the predictions significantly on databases consisting of masked preferences even though a few of the attack strategies are not effective in a privacy-preserving environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.7
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• pp.3356-3374
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• 2018

The healthcare and fitness wearable-device market is considered as the driving force of the entire wearable device market. However, there are concerns with respect to information privacy because wearable devices constantly collect sensitive data such as individuals' health information. Thus, there is a need for a comprehensive understanding from the perspective of information privacy concerns and related behavior. This study investigates factors considered in the privacy calculus of wearable fitness devices, and verifies differences obtained by the privacy calculus process according to the frequency of exercise. The results obtained from a survey of 248 undergraduate students in Korea revealed that service providers should consider users' interests and exercise characteristics in order to mitigate their privacy concerns and encourage continuous use of wearable devices. This study provides useful insights pertaining to users of wearable fitness devices, and targets researchers and practitioners.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.3
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• pp.1028-1046
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• 2022

Genome-wide association studies (GWAS) aim to find the significant genetic variants for common complex disease. However, genotype data has privacy information such as disease status and identity, which make data sharing and research difficult. Differential privacy is widely used in the privacy protection of data sharing. The current differential privacy approach in GWAS pays no attention to raw data but to statistical data, and doesn't achieve equilibrium between utility and privacy, so that data sharing is hindered and it hampers the development of genomics. To share data more securely, we propose a differential privacy preserving approach of data sharing for GWAS, and achieve the equilibrium between privacy and data utility. Firstly, a reasonable disturbance interval for the genotype is calculated based on the expected utility. Secondly, based on the interval, we get the Nash equilibrium point between utility and privacy. Finally, based on the equilibrium point, the original genotype matrix is perturbed with differential privacy, and the corresponding random genotype matrix is obtained. We theoretically and experimentally show that the method satisfies expected privacy protection and utility. This method provides engineering guidance for protecting GWAS data privacy.

• Journal of the Korean Society of Clothing and Textiles
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• v.27 no.7
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• pp.746-757
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• 2003

The purpose of this study was to investigate the internet perceived risk segments in regard to clothing benefits sought, internet shopping attitude, and internet purchase intention. The subjects used for the study were 210 male and 338 female college students. The internet perceived risk consisted of size/defect risk, social psychological risk, privacy risk, delivery risk, and price risk. The clothing benefits sought had impression improvement, fashion, individuality, figure flaws compensation, and comfort factors. The results showed that consumers were segmented by four groups based on internet perceived risk factors : 1) privacy risk group, 2) size risk group. 3) low risk group, and 4) price/social psychological risk group. The four segmented groups differed in regard to clothing benefits sought, internet shopping attitude, and internet purchase intention. For example, in regard to clothing benefits sought, the price/social Psychological risk group sought fashion more than other groups. The low risk group considered figure flaws compensation benefit less important than other groups. Concerning internet shopping attitude, the low risk group had more favorable altitude toward trust, safety, diversity, exchange/return attributes of internet shopping than other groups. The privacy risk group had more favorable attitude toward convenience and price attributes of internet shopping. Regarding internet purchase intention, the low risk group had higher intention to purchase formal, casual, and sportswear. The size risk group had higher intention to purchase fashion accessories. Further group differences and implications of the results were discussed.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.30 no.10B
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• pp.648-659
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• 2005

One's identity on the Internet has been disclosed and abused without his consent. Personal information must be protected by appropriate security safeguard. An Individual should have the right to know whether his personal details have been collected and stored. This paper proposes various conceptual models for designing privacy enabling service architecture in the Internet identity management system. For the restriction of access to personal information, we introduce the owner's policy and the management policy The owner's policy should provide the user with enough information to manage easily and securely his data. To control precisely and effectively all personal information in the Identity provider, we propose the privacy management policy and the privacy authorization model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.7 no.3
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• pp.558-575
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• 2013

The purpose of a data leakage prevention system is to protect corporate information assets. The system monitors the packet exchanges between internal systems and the Internet, filters packets according to the data security policy defined by each company, or discretionarily deletes important data included in packets in order to prevent leakage of corporate information. However, the problem arises that the system may monitor employees' personal information, thus allowing their privacy to be violated. Therefore, it is necessary to find not only a solution for detecting leakage of significant information, but also a way to minimize the leakage of internal users' personal information. In this paper, we propose two models for representing the level of personal information disclosure during data leakage detection. One model measures only the disclosure frequencies of keywords that are defined as personal data. These frequencies are used to indicate the privacy violation level. The other model represents the context of privacy violation using a private data matrix. Each row of the matrix represents the disclosure counts for personal data keywords in a given time period, and each column represents the disclosure count of a certain keyword during the entire observation interval. Using the suggested matrix model, we can represent an abstracted context of the privacy violation situation. Experiments on the privacy violation situation to demonstrate the usability of the suggested models are also presented.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.9
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• pp.4463-4483
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• 2019

Privacy-preserving collaborative filtering schemes take privacy concerns into its primary consideration without neglecting the prediction accuracy. Different schemes are proposed that are built upon different data partitioning scenarios such as a central server, two-, multi-party or peer-to-peer network. These data partitioning scenarios have been investigated in terms of claimed privacy promises, recently. However, to the best of our knowledge, any peer-to-peer privacy-preserving scheme lacks such study that scrutinizes privacy promises. In this paper, we apply three different attack techniques by utilizing auxiliary information to derive private ratings of peers and conduct experiments by varying privacy protection parameters to evaluate to what extent peers' data can be reconstructed.