• Convergence Security Journal
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• v.9 no.4
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• pp.29-34
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• 2009

The many transaction occurs because of service will not be able to trust in mobile Ad-hoc Network. Overcomes like this problem point with the plan for, This paper proposes the trust evaluation mechanism which is the possibility becoming the standard of the node selection which provides a better service by using the transaction message which is basic occurs from within the mobile Ad-hoc network and in the transaction message which is additional. At last, this paper embodies the trust mechanism which proposes, confirms the trust value which is evaluated with NS2 simulator.

• Journal of Digital Contents Society
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• v.9 no.1
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• pp.33-39
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• 2008

Mobile Ad Hoc Network is easy to be attacked because nodes are distributed not network based infrastructure. Intrusion detection system perceives the trust values of neighboring nodes and receives inspection on local security of nodes and observation ability. This study applied clustering mechanism to reduce overhead in intrusion detection. And, in order to measure the trust values, it associates the trust information cluster head received from member nodes with its own value and evaluates the trust of neighboring nodes. Secure data transmission is received by proposed concept because the trust of nodes on network is achieved accurately.

• Proceedings of the Korea Database Society Conference
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• 2010.06a
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• pp.277-287
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• 2010

We are sometimes interacting with people who we know nothing and facing with the difficult task of making decisions involving risk in social network. To reduce risk, the topic of building Web of trust is receiving considerable attention in social network. The easiest approach to build Web of trust will be to ask users to represent level of trust explicitly toward another users. However, there exists sparsity issue in Web of trust which is represented explicitly by users as well as it is difficult to urge users to express their level of trustworthiness. We propose a fuzzy-based inference model for Web of trust using user behavior information in social network. According to the experiment result which is applied in Epinions.com, the proposed model show improved connectivity in resulting Web of trust as well as reduced prediction error of trustworthiness compared to existing computational model.

• Journal of Information Technology Services
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• v.11 no.2
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• pp.319-337
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• 2012

To stress the importance of privacy in social networking, I presented an analysis on how information control and information management vulnerability influence trust and privacy concerns in social networking, and how trust and privacy concerns influence the sustainable usage intention of social network services. I also analyzed the factors affecting privacy concerns to present the method to alleviate social network users' concerns about privacy. Information collection control, information processing control and information management vulnerability were chosen and analyzed as the factors affecting privacy concerns. The results showed that information collection control and information management vulnerability significantly affected trust and privacy concerns; and information processing control did not significantly affect privacy concerns. The relationship between trust and privacy concerns, and sustainable usage intention was statistically significant; and the relationship between trust and expansion of communication channels was also statistically significant.

• Journal of Korea Society of Digital Industry and Information Management
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• v.4 no.1
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• pp.33-40
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• 2008

This paper presents a new scheduling scheme in wireless distributed network. To overcome the limited information about unfamiliar mobile nodes and to reduce the required system performance, we propose a scheduling algorithm of job allocation based on the trust model. The suggested scheduler evaluate an unfamiliar mobile node's trust and make reference to the trust value of neighboring scheduler. This scheduling algorithm can avoid malicious or selfish mobile nodes by assigning low trust values. We also present a trust evaluation metric and show the efficiency of suggested scheduling algorithm by performance evaluation.

• ETRI Journal
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• v.27 no.4
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• pp.465-468
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• 2005

This paper presents a new trust evaluation scheme in an ad hoc network. To overcome the limited information about unfamiliar nodes and to reduce the required memory space, we propose a cluster-based trust evaluation scheme, in which neighboring nodes form a cluster and select one node as a cluster head. The head issues a trust value certificate that can be referred to by its non-neighbor nodes. In this way, an evaluation of an unfamiliar node's trust can be done very efficiently and precisely. In this paper, we present a trust evaluation metric using this scheme and some operations for forming and managing a cluster. An analysis of the proposed scheme over some security problems is also presented.

• Journal of Information Technology Applications and Management
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• v.20 no.4
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• pp.191-205
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• 2013

Recently, the topic of predicting interpersonal trust in online social networks is receiving considerable attention, because trust plays a critical role in controlling the spread of distorted information and vicious rumors, as well as reducing uncertainties and risk from unreliable users in social networks. Several trust prediction models have been developed on the basis of transitivity and composability properties of trust; however, it is hard to find empirical studies on whether and how transitivity and composability properties of trust are operated in real online social networks. This study aims to predict interpersonal trust between two unknown users in social networks and verify the proposition on whether and how transitivity and composability of trust are operated in social networks. For this purpose, we chose three social network sites called FilmTrust, Advogato, and Epinion, which contain explicit trust information by their users, and we empirically investigated the proposition. Experimental results showed that trust can be propagated farther and farther along the trust link; however, when path distance becomes distant, the accuracy of trust prediction lowers because noise is activated in the process of trust propagation. Also, the composability property of trust is operated as we expected in real social networks. However, contrary to our expectations, when the path is synthesized more during the trust prediction, the reliability of predicted trust did not tend to increase gradually.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.113-127
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• 2016

Among the techniques for recommendation, collaborative filtering (CF) is commonly recognized to be the most effective for implementing recommender systems. Until now, CF has been popularly studied and adopted in both academic and real-world applications. The basic idea of CF is to create recommendation results by finding correlations between users of a recommendation system. CF system compares users based on how similar they are, and recommend products to users by using other like-minded people's results of evaluation for each product. Thus, it is very important to compute evaluation similarities among users in CF because the recommendation quality depends on it. Typical CF uses user's explicit numeric ratings of items (i.e. quantitative information) when computing the similarities among users in CF. In other words, user's numeric ratings have been a sole source of user preference information in traditional CF. However, user ratings are unable to fully reflect user's actual preferences from time to time. According to several studies, users may more actively accommodate recommendation of reliable others when purchasing goods. Thus, trust relationship can be regarded as the informative source for identifying user's preference with accuracy. Under this background, we propose a new hybrid recommender system that fuses CF and social network analysis (SNA). The proposed system adopts the recommendation algorithm that additionally reflect the result analyzed by SNA. In detail, our proposed system is based on conventional memory-based CF, but it is designed to use both user's numeric ratings and trust relationship information between users when calculating user similarities. For this, our system creates and uses not only user-item rating matrix, but also user-to-user trust network. As the methods for calculating user similarity between users, we proposed two alternatives - one is algorithm calculating the degree of similarity between users by utilizing in-degree and out-degree centrality, which are the indices representing the central location in the social network. We named these approaches as 'Trust CF - All' and 'Trust CF - Conditional'. The other alternative is the algorithm reflecting a neighbor's score higher when a target user trusts the neighbor directly or indirectly. The direct or indirect trust relationship can be identified by searching trust network of users. In this study, we call this approach 'Trust CF - Search'. To validate the applicability of the proposed system, we used experimental data provided by LibRec that crawled from the entire FilmTrust website. It consists of ratings of movies and trust relationship network indicating who to trust between users. The experimental system was implemented using Microsoft Visual Basic for Applications (VBA) and UCINET 6. To examine the effectiveness of the proposed system, we compared the performance of our proposed method with one of conventional CF system. The performances of recommender system were evaluated by using average MAE (mean absolute error). The analysis results confirmed that in case of applying without conditions the in-degree centrality index of trusted network of users(i.e. Trust CF - All), the accuracy (MAE = 0.565134) was lower than conventional CF (MAE = 0.564966). And, in case of applying the in-degree centrality index only to the users with the out-degree centrality above a certain threshold value(i.e. Trust CF - Conditional), the proposed system improved the accuracy a little (MAE = 0.564909) compared to traditional CF. However, the algorithm searching based on the trusted network of users (i.e. Trust CF - Search) was found to show the best performance (MAE = 0.564846). And the result from paired samples t-test presented that Trust CF - Search outperformed conventional CF with 10% statistical significance level. Our study sheds a light on the application of user's trust relationship network information for facilitating electronic commerce by recommending proper items to users.

• Journal of Information Technology Applications and Management
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• v.20 no.2
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• pp.177-191
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• 2013

Social network has been expected to increase the value of social capital through online user interactions which remove geographical boundary. However, online users in social networks face challenges of assessing whether the anonymous user and his/her providing information are reliable or not because of limited experiences with a small number of users. Therefore. it is vital to provide a successful trust model which builds and maintains a web of trust. This study aims to propose a prediction method for the interpersonal trust which measures the level of trust about information provider in Facebook. To develop the prediction method. we first investigated behavioral research for trust in social science and extracted 5 antecedents of trust : lenience, ability, steadiness, intimacy, and similarity. Then we measured the antecedents from the history of interactive behavior and built prediction models using the two decision trees and a computational model. We also applied the proposed method to predict interpersonal trust between Facebook users and evaluated the prediction accuracy. The predicted trust metric has dynamic feature which can be adjusted over time according to the interaction between two users.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.1
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• pp.291-299
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• 2020

The purpose of this study focuses on finding the influences of social capital on personal happiness by studying service industry employees. This current study utilized a questionnaire survey method which was used to collect the analysis data, from July 20 to August 10 in 2019. 281 questionnaires were gathered in Korea and the statistical analysis was conducted. This study recognized social capital as 3 independent variables and personal happiness as a dependent variable. Social capital is composed of the social network, social trust and social norms. First, the social network consisted of the satisfaction degree in one's social relations and the social interaction degree. Second, social trust is composed of the trust degree with ones neighbors in the same local area. Last, social norms are consisted of reciprocity, participation and a sense of belonging one feels in the same society. The findings of this study were as follows: first, it was found that social network, social trust, and social norms made affirmative influences on personal happiness of the employees. Second, the social network and social norms made statistically significant influences with personal happiness, but social trust was shown to not have similar influence.