• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.2
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• pp.317-322
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• 2013

Personal information is defined information related to users' privacy data. It can be verified information through social security number, image, and means relating to individual can verify. Such personal information is in accordance with the privacy act in law for the collection and usage in enterprises and institutions. However, it can be induced privacy problem when it is exposed information without attention. This user's inadvertent disclosure of personal information has occurred due to social engineering and intelligent cyber-crime occurred in order to solve these problems. A variety of protection solutions for personal information have been developed. Web privacy filtering firewall and solutions related with server have been developed among developed many solutions, web privacy filtering and firewall solutions is proposed in this paper.

• The Journal of Information Systems
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• v.25 no.4
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• pp.1-21
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• 2016

Purpose The study analyzed the factors affecting continuance use and self-disclosure in the SNS(social networking service) context based on a dual-factor model. As SNS users have concerned privacy for a long time, privacy concern affects continuous use and self-disclosure. In details, concern over privacy may have a stronger effect on self-disclosure than on continuance use as users' personal information can be more exposed during posting their dailies and photos. Design/Methodology/Approach SNS benefits, trust in SNS providers, and social influence are served as the key enablers and privacy concern as the inhibitor. Moreover, the relative impacts of SNS benefits and privacy concern on continuance use and self-disclosure were analysed in this study. From the data of 327 Facebook users, the researchers tested proposed theoretical model by using PLS. Findings Users' continuance intention and self-disclosure behavior are differently affected by different antecedents. Trust in SNS provider had a significant effect on self-disclosure intention, while it has no significant effect on continuance intention. Concern over privacy was negatively related to self-disclosure intention, while it was positively associated with continuance intention.

• The Journal of Korean Academic Society of Nursing Education
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• v.24 no.3
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• pp.225-234
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• 2018

Purpose: The purpose of this study was to identify factors influencing patient privacy protection behavior among nursing students and examine the relationships between these factors. Methods: Participants in this study were 144 nursing students who have experienced clinical practice. The data were analyzed using descriptive statistics, one-way ANOVA, Scheffe test, Pearson's correlation coefficient, and multiple regression with IBM SPSS Win 23.0 program. Results: Professional self-concept and ethical values were factors influencing patient privacy protection behavior among nursing students. These variables explained 21.9% of the variance for patient privacy protection behavior. A higher level of patient privacy protection behavior was associated with higher levels of professional self-concept and ethical values. Conclusion: The findings demonstrate that strategies for enhancing patient privacy protection behaviors of nursing students should include methods for forming images of positive nurses and firming ethical values.

• 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.15 no.2
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• pp.540-557
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• 2021

Privacy vulnerability of social networks is one of the major concerns for social science research and business analysis. Most existing studies which mainly focus on un-weighted network graph, have designed various privacy models similar to k-anonymity to prevent data disclosure of vertex attributes or relationships, but they may be suffered from serious problems of huge information loss and significant modification of key properties of the network structure. Furthermore, there still lacks further considerations of privacy protection for important sensitive edges in weighted social networks. To address this problem, this paper proposes a privacy preserving method to protect sensitive weighted edges. Firstly, the sensitive edges are differentiated from weighted edges according to the edge betweenness centrality, which evaluates the importance of entities in social network. Then, the perturbation operations are used to preserve the privacy of weighted social network by adding some pseudo-edges or modifying specific edge weights, so that the bottleneck problem of information flow can be well resolved in key area of the social network. Experimental results show that the proposed method can not only effectively preserve the sensitive edges with lower computation cost, but also maintain the stability of the network structures. Further, the capability of defending against malicious attacks to important sensitive edges has been greatly improved.

• Asia-Pacific Journal of Business
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• v.15 no.2
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• pp.107-123
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• 2024

Purpose - This study aims to analyze the relationship between perceived risk, privacy concerns, continual intention to use of Fintech services. Design/methodology/approach - This research developed a conceptual framework using attitude theory and analyzed the relationship between risk perception to Fintech services, Fintech concerns(service concern, platform concern, network concern, device concern, and privacy concern), and continual use in the context of Fintech services. In this study, Data analyzed using the PLS(partial least squares) structural equation model approach. Findings - As a result of empirical analysis, Fintech risk perception affected service concern, platform concern, network concern, device concern, and privacy concern. In addition, it was found that privacy risk did not affect continual use of Fintech services. In addition, among Fintech service concerns, platform concern, network concern, and privacy concern did not affect continual use of Fintech services. However, only device concern affected the intention to continual use of Fintech services. Research implications or Originality - The results will help to understand the psychology of Fintech service users and develop more stable Fintech services continual use strategies.

• The Journal of the Korea institute of electronic communication sciences
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• v.8 no.3
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• pp.379-387
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• 2013

In large-scale wireless sensor networks, tremendous amount of dummy packets is usually accompanied by keeping location privacy of the communication source and destination against global eavesdropping. In our earlier work we designed a location privacy routing protocol, ELPR(End-node Location Privacy Routing) in which the generation of dummy packets at each idle time-slot while transferring data packets are restricted to only the nodes within certain areas of encompassing the source and destination, respectively. In this paper, it is given that ELPR provides various degrees of location privacy while PCM(Periodic Collection Method) allows the only fixed level. Simulation results show that as the number of nodes or data packets increases ELPR permits in terms of the number of generated packets more cost-effective location privacy than PCM.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.26 no.4
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• pp.1013-1023
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• 2016

Transborder data flow(TBDF) of personal information in Korea has been limited by current Privacy law which request data subject to give consent. As the IT industry is growing at an incredible rate, there is a need to review the existing law to cope with growing industrial demand and increasing numbers of international data transfer. The transfer of personal data overseas not only allow businesses providing IT services including finance, internet, e-commerce to thrive, but also impact every aspect of our lives which are increasingly depended on these technology. Transmitting personal data across borders raises serious questions of privacy protection and restriction of business operation. In ordrer to promote interoperability of personal data in international environment, a considerable amount of research and debate needs to be taken before implementing a sound policy. This paper presents a need for a sound TBDF policy in Korea by examine the main policy challenges associated with TBDF. Finally, the paper identify policy suggestions based on European Union's approach as they have successfully implemented TBDF policy that balanced data privacy and economic agenda.

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

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.3
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• pp.958-979
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• 2023

Due to laws, regulations, privacy, etc., between 70-90 percent of providers do not share medical data, forming a "data island". It is essential to collaborate across multiple institutions without sharing patient data. Most existing methods adopt distributed learning and centralized federal architecture to solve this problem, but there are problems of resource heterogeneity and data heterogeneity in the practical application process. This paper proposes a collaborative deep learning modelling method based on the blockchain network. The training process uses encryption parameters to replace the original remote source data transmission to protect privacy. Hyperledger Fabric blockchain is adopted to realize that the parties are not restricted by the third-party authoritative verification end. To a certain extent, the distrust and single point of failure caused by the centralized system are avoided. The aggregation algorithm uses the FedProx algorithm to solve the problem of device heterogeneity and data heterogeneity. The experiments show that the maximum improvement of segmentation accuracy in the collaborative training mode proposed in this paper is 11.179% compared to local training. In the sequential training mode, the average accuracy improvement is greater than 7%. In the parallel training mode, the average accuracy improvement is greater than 8%. The experimental results show that the model proposed in this paper can solve the current problem of centralized modelling of multicenter data. In particular, it provides ideas to solve privacy protection and break "data silos", and protects all data.