• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2014.10a
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• pp.131-134
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• 2014

The Internet allows firms to serve customers more effectively than ever before. In the B2C context, we examine the interrelationships among information privacy concerns, e-trust, and e-loyalty. The authors extend prior research by incorporating constructs of information privacy concerns into a more holistic conceptual framework. This study answers three research questions: Will the three components of information privacy concerns have a significant effect on e-loyalty through e-trust?, Does e-trust mediate e-loyalty?, Finally, do switching costs have a moderation effect between e-trust and e-loyalty?, The authors examine data from customers who have booked hotel accommodations online. The results support our hypotheses and confirm both the mediation role of e-trust and the moderation role of switching costs. Conceptually, this study provides an empirical validation of information privacy concerns, e-trust, and loyalty linkage. On the managerial level, this research provides insights into critical drivers of loyalty in the emerging online marketplace.

• Journal of the Korea Society of Computer and Information
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• v.29 no.6
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• pp.69-76
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• 2024

Recent advances in sensor and mobile technologies have made it possible to collect user location data. This location information is used as a valuable asset in various industries, resulting in increased demand for location data collection and sharing. However, because location data contains sensitive user information, indiscriminate collection can lead to privacy issues. Recently, geo-indistinguishability (Geo-I), a method of differential privacy, has been widely used to protect the privacy of location data. While Geo-I is powerful in effectively protecting users' locations, it poses a problem because the utility of the collected location data decreases due to data perturbation. Therefore, this paper proposes a method using Geo-I technology to effectively collect user location data while maintaining its data utility. The proposed method utilizes the prior distribution of users to improve the overall data utility, while protecting accurate location information. Experimental results using real data show that the proposed method significantly improves the usefulness of the collected data compared to existing methods.

• Journal of Information Processing Systems
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• v.7 no.1
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• pp.111-120
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• 2011

The concept of Smart-Homes is becoming more and more popular. It is anticipated that Radio Frequency IDentification (RFID) technology will play a major role in such environments. We can find many previously proposed schemes that focus solely on: authentication between the RFID tags and readers, and user privacy protection from malicious readers. There has also been much talk of a very popular RFID application: a refrigerator/bookshelf that can scan and list out the details of its items on its display screen. Realizing such an application is not as straight forward as it seems to be, especially in securely deploying such RFID-based applications in a smart home environment. Therefore this paper describes some of the RFID-based applications that are applicable to smart home environments. We then identify their related privacy and security threats and security requirements and also propose a secure approach, where RFID-tagged consumer items, RFID-reader enabled appliances (e.g., refrigerators), and RFID-based applications would securely interact among one another. At the moment our approach is just a conceptual idea, but it sheds light on very important security issues related to RFID-based applications that are beneficial for consumers.

• International journal of advanced smart convergence
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• v.8 no.2
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• pp.204-210
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• 2019

Mobile Crowd Computing is one of the most efficient and effective way to collect the Electronic health records and they are very intelligent in processing them. Mobile Crowd Computing can handle, analyze and process the huge volumes of Electronic Health Records (EHR) from the high-performance Cloud Environment. Electronic Health Records are very sensitive, so they need to be secured, authenticated and processed efficiently. However, security, privacy and authentication of Electronic health records(EHR) and Patient health records(PHR) in the Mobile Crowd Computing Environment have become a critical issue that restricts many healthcare services from using Crowd Computing services .Our proposed Efficient Multi-layer Encryption Framework(MLEF) applies a set of multiple security Algorithms to provide access control over integrity, confidentiality, privacy and authentication with cost efficient to the Electronic health records(HER)and Patient health records(PHR). Our system provides the efficient way to create an environment that is capable of capturing, storing, searching, sharing, analyzing and authenticating electronic healthcare records efficiently to provide right intervention to the right patient at the right time in the Mobile Crowd Computing Environment.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.5
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• pp.1223-1237
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• 2024

Autonomous vehicles use onboard sensors to sense the surrounding environment. In complex autonomous driving scenarios, the detection and recognition capabilities are constrained, which may result in serious accidents. An efficient way to enhance the detection and recognition capabilities is establishing collaborations with the neighbor vehicles. However, the collaborations introduce additional challenges in terms of the data heterogeneity, communication cost, and data privacy. In this paper, a novel personalized federated learning framework is proposed for addressing the challenges and enabling efficient collaborations in autonomous driving environment. For obtaining a global model, vehicles perform local training and transmit logits to a central unit instead of the entire model, and thus the communication cost is minimized, and the data privacy is protected. Then, the inference similarity is derived for capturing the characteristics of data heterogeneity. The vehicles are divided into clusters based on the inference similarity and a weighted aggregation is performed within a cluster. Finally, the vehicles download the corresponding aggregated global model and train a personalized model which is personalized for the cluster that has similar data distribution, so that accuracy is not affected by heterogeneous data. Experimental results demonstrate significant advantages of our proposed method in improving the efficiency of collaborative perception and reducing communication cost.

• Electronics and Telecommunications Trends
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• v.19 no.5 s.89
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• pp.95-106
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• 2004

유비쿼터스 네트워크는 언제, 어디서나, 어떤 기기로도 고품질의 다양한 유비쿼터스 서비스를 가능하게할 것이다. 디지털 홈, 전자정부, e-비즈니스 서비스를 제공하기 위해서 통신서비스, 방송서비스, 인터넷서비스가 BcN을 중심으로 컨버전스 된다고 볼 수 있다. 안전한 유비쿼터스 환경을 위해서는 새롭게 제안하는 fusion security framework의 조기정착이 필수적이며, 이를 위한 기술적 측면의 정보보호와 법적/제도적 측면의 정보보호를 알아보고, 향후의 유비쿼터스 정보보호에 대한 정책적 방향을 제안하고자 한다. 특히, 기술적인 측면에서의 국가 정책방향은 크게 인터넷 안전 대응체계 강화를 위한 u-secureKorea, 깨끗한 사이버 환경 구현을 위한 u-clean Korea, 개인정보보호 환경 구현을 위한 u-privacyKorea의 3가지 분야로 구분할 수 있으며, 3개 분야별로 BcN 인프라 보호기술 개발, 정보침해 방지기술개발, 개인정보 보호기술 개발을 추진하여야 하는 당위성을 제시하고자 한다.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.177-193
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• 2014

Over the past decade, there has been a rapid diffusion of electronic commerce and a rising number of interconnected networks, resulting in an escalation of security threats and privacy concerns. Electronic commerce has a built-in trade-off between the necessity of providing at least some personal information to consummate an online transaction, and the risk of negative consequences from providing such information. More recently, the frequent disclosure of private information has raised concerns about privacy and its impacts. This has motivated researchers in various fields to explore information privacy issues to address these concerns. Accordingly, the necessity for information privacy policies and technologies for collecting and storing data, and information privacy research in various fields such as medicine, computer science, business, and statistics has increased. The occurrence of various information security accidents have made finding experts in the information security field an important issue. Objective measures for finding such experts are required, as it is currently rather subjective. Based on social network analysis, this paper focused on a framework to evaluate the process of finding experts in the information security field. We collected data from the National Discovery for Science Leaders (NDSL) database, initially collecting about 2000 papers covering the period between 2005 and 2013. Outliers and the data of irrelevant papers were dropped, leaving 784 papers to test the suggested hypotheses. The co-authorship network data for co-author relationship, publisher, affiliation, and so on were analyzed using social network measures including centrality and structural hole. The results of our model estimation are as follows. With the exception of Hypothesis 3, which deals with the relationship between eigenvector centrality and performance, all of our hypotheses were supported. In line with our hypothesis, degree centrality (H1) was supported with its positive influence on the researchers' publishing performance (p<0.001). This finding indicates that as the degree of cooperation increased, the more the publishing performance of researchers increased. In addition, closeness centrality (H2) was also positively associated with researchers' publishing performance (p<0.001), suggesting that, as the efficiency of information acquisition increased, the more the researchers' publishing performance increased. This paper identified the difference in publishing performance among researchers. The analysis can be used to identify core experts and evaluate their performance in the information privacy research field. The co-authorship network for information privacy can aid in understanding the deep relationships among researchers. In addition, extracting characteristics of publishers and affiliations, this paper suggested an understanding of the social network measures and their potential for finding experts in the information privacy field. Social concerns about securing the objectivity of experts have increased, because experts in the information privacy field frequently participate in political consultation, and business education support and evaluation. In terms of practical implications, this research suggests an objective framework for experts in the information privacy field, and is useful for people who are in charge of managing research human resources. This study has some limitations, providing opportunities and suggestions for future research. Presenting the difference in information diffusion according to media and proximity presents difficulties for the generalization of the theory due to the small sample size. Therefore, further studies could consider an increased sample size and media diversity, the difference in information diffusion according to the media type, and information proximity could be explored in more detail. Moreover, previous network research has commonly observed a causal relationship between the independent and dependent variable (Kadushin, 2012). In this study, degree centrality as an independent variable might have causal relationship with performance as a dependent variable. However, in the case of network analysis research, network indices could be computed after the network relationship is created. An annual analysis could help mitigate this limitation.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.6
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• pp.8-18
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• 2010

As Ubiquitous systems are developed, the number of context data which are dealt with systems also grow rapidly. In these data, some are vary important in privacy view. As these data are given to users or services of systems, probability of excess exposing of data is exist. To solve this problem, many systems use access control method like RBAC. But even this method can avoid unauthenticated access, can not prevent excess exposing of authenticated access. To prevent this exposing of context data, this paper suggests context data access control framework which abstracts context data when system gives these data to users or services. Using negotiation protocol and context data abstraction technique using RDF, our framework prevents excess exposing important data. This happens protecting privacy and keeping service continuity.

• KIISE Transactions on Computing Practices
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• v.21 no.11
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• pp.733-738
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• 2015

User demographic information is necessary in order to improve the quality of personalized services such as recommendation systems. Mobile data, especially text data, is known to be effective for prediction of user demographic information. However, mobile text data has privacy issues so that its utilization is limited. In this regard, we introduce an on-device gender prediction framework utilizing mobile text data while minimizing the privacy issue. Discriminative word and emoticon sets of each gender are constructed from web documents written by authors of each gender. After gender prediction is performed by comparing discriminative word and emoticon sets with a user's mobile text data, an ensemble method that combines two prediction results draws a final result. From experiments conducted on real-world mobile text data, the proposed on-device framework shows promising results for gender prediction.

• Journal of Digital Forensics
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• v.12 no.3
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• pp.39-54
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• 2018

Recently, discussions for the eradication of illegal shooting have been carried out in a socially-oriented way. The government has established comprehensive measures to eradicate cyber sexual violence crimes such as illegal shooting. Although the social interest in illegal shooting has increased, the illegal film shooting case is evolving more and more due to the development of information and communication technology. Applications that can hide confused videos are constantly circulating around the market and community sites. As a result, field investigators and professional analysts are experiencing difficulties in collecting and analyzing evidence. In this paper, we propose an evidence collection and analysis framework for illegal shooting cases in order to give practical help to illegal shooting investigation. We also proposed a system that can detect hidden applications, which is one of the main obstacles in evidence collection and analysis. We developed a detection tool to evaluate the effectiveness of the proposed system and confirmed the feasibility and scalability of the system through experiments using commercially available concealed apps.