• KSII Transactions on Internet and Information Systems (TIIS)
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• v.4 no.4
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• pp.547-559
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• 2010

As web-based online communities are rapidly growing, the agents in the communities need to know their measurable belief of trust for safe and successful interactions. In this paper, we propose a computational model of trust resulting from available feedbacks in online communities. The notion of trust can be defined as an aggregation of consensus given a set of past interactions. The average trust of an agent further represents the center of gravity of the distribution of its trustworthiness and untrustworthiness. Furthermore, we precisely describe the relationships among reputation, trust and average trust through concrete examples showing their computations. We apply our trust model to online social networks in order to show how trust mechanisms are involved in the rational purchasing decision-making of buyers and sellers, and we summarize our simulation results.

• Journal of Internet Computing and Services
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• v.8 no.4
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• pp.137-147
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• 2007

As Web-based online communities are rapidly growing, the agents in social groups need to know their measurable belief of trust for safe andsuccessful interactions. In this paper, we propose a computational model of trust resulting from available feedbacks in online communities. The notion of trust can be defined as an aggregation of consensus given a set of past interactions. The averagetrust of an agent further represents the center of gravity of the distribution of its trustworthiness and untrustworthiness. And then, we precisely describe the relationship between reputation, trust, and averagetrust through a concrete example of their computations. We apply our trust model to online internet settings in order to show how trust mechanisms are involved in a rational decision-making of the agents.

• Journal of Information Technology Applications and Management
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• v.17 no.4
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• pp.39-56
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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.

• 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 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.

• Journal of applied mathematics & informatics
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• v.19 no.1_2
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• pp.165-177
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• 2005

In this paper, an adaptive trust region method based on the conic model for unconstrained optimization problems is proposed and analyzed. We establish the global and super linear convergence results of the method. Numerical tests are reported that confirm the efficiency of the new method.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.1
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• pp.61-65
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• 2006

This paper is concerned with a quantitative computational trust model which lakes into account multiple evaluation criteria and uses the recommendation from others in order to get the trust value for entities. In the proposed trust model, the trust for an entity is defined as the expectation for the entity to yield satisfactory outcomes in the given situation. Once an interaction has been made with an entity, it is assumed that outcomes are observed with respect to evaluation criteria. When the trust information is needed, the satisfaction degree, which is the probability to generate satisfactory outcomes for each evaluation criterion, is computed based on the outcome probability distributions and the entity's preference degrees on the outcomes. Then, the satisfaction degrees for evaluation criteria are aggregated into a trust value. At that time, the reputation information is also incorporated into the trust value. This paper presents in detail how the trust model works.

• Journal of Software Assessment and Valuation
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• v.15 no.2
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• pp.129-140
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• 2019

In the IoT environment, there is a huge amount of heterogeneous devices with limited capacity. Existing trust evaluation methods are not adequate to accommodate this requirement due to the limited storage space and computational resources. In addition, since IoT devices are mainly human operated devices, the trust evaluation should reflect the social relations among device owners. There is also a need for a mechanism that reflects the tendency of the trustor and environmental factors. In this paper, we propose an adaptable trust evaluation method for SOA-based IoT system to deal with these issues. The proposed model is designed to minimize the confidence bias and to dynamically respond to environmental changes by combining direct evaluation and indirect evaluation. It is expected that it will be possible to secure trust through quantitative evaluation by providing feedback based on social relationships.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.3
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• pp.487-499
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• 2022

Machine learning such as deep learning have been widely used in recent years. Recently deep learning is performed in a trusted execution environment such as ARM TrustZone to improve security in edge devices and embedded devices with low computing resource. To mitigate this problem, we propose TPMP that efficiently uses the limited memory of TEE through DNN model partitioning. TPMP achieves high confidentiality of DNN by performing DNN models that could not be run with existing memory scheduling methods in TEE through optimized memory scheduling. TPMP required a similar amount of computational resources to previous methodologies.

• The KIPS Transactions:PartB
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• v.13B no.7 s.110
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• pp.633-642
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• 2006

In the Internet environment many interactions between many users and unknown users take place and it is usually rare to have the trust information about others. Due to the lack of trust information, entities have to take some risks in transactions with others. In this perspective, it is crucial for the entities to be equipped with functionality to accumulate and manage the trust information on other entities in order to reduce risks and uncertainty in their transactions. This paper is concerned with a quantitative computational trust model which takes into account multiple evaluation criteria and uses the recommendation from others in order to get the trust for an entity. In the proposed trust model, the trust for an entity is defined as the expectation for the entity to yield satisfactory outcomes in the given situation. Once an interaction has been made with an entity, it is assumed that outcomes are observed with respect to evaluation criteria. When the trust information is needed, the satisfaction degree, which is the probability to generate satisfactory outcomes for each evaluation criterion, is computed based on the empirical outcome outcome distributions and the entity's preference degrees on the outcomes. Then, the satisfaction degrees for evaluation criteria are aggregated into a trust value. At that time, the reputation information is also incorporated into the trust value. This paper also shows that the model could help the entities effectively choose other entities for transactions with some experiments in e-commerce.