• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.4
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• pp.1516-1528
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• 2015

We introduce attribute set based signature (ASBS), a new cryptographic primitive which organizes user attributes into a recursive set based structure such that dynamic constraints can be imposed on how those attributes may be combined to satisfy a signing policy. Compared with attribute based signature (ABS), ASBS is more flexible and efficient in managing user attributes and specifying signing policies. We present a practical construction of ASBS and prove its security in the standard model under three subgroup decision related assumptions. Its efficiency is comparable to that of the most efficient ABS scheme.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.46 no.1
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• pp.78-87
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• 2009

An attribute-based signature scheme is a signature scheme where a signer's private key is associate with an attribute set and a signature is associated with an access structure. Attribute-based signature schemes are useful to provide anonymity and access control for role-based systems and attribute-based systems where an identity of object is represented as a set of roles or attributes. In this paper, we formally define the definition of attribute-based signature schemes and propose the first efficient attribute-based signature scheme that requires constant number of pairing operations for verification where a policy is represented as a disjunctive normal form (DNF). To construct provably secure one, we introduce a new interactive assumption and prove that our construction is secure under the new interactive assumption and the random oracle model.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.20 no.43
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• pp.321-331
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• 1997

Measurement of attributes is often highly subjective and imprecise, yet most MADM methods lack provisions for handling imprecise data. Frequently, decision makers must establish a ranking within a finite set of alternatives with respect to multiple attributes which have varying degrees of importance. The problem is more complex if the evaluations of alternatives according to each attribute are not expressed in precise numbers, but rather in fuzzy numbers. Analysis must allow for lack of precision and partial truth. The advantages of a fuzzy approach for MADM are that a decision maker can obtain efficient solutions all at once without trial and error, and that this approach provides better support for judging the interactive improvement of solutions in comparison with o decision making method. The algorithm used in this study is based on the concepts of vague set theory. Linguistic variables and vague values are used to facilitate a decision maker's subjective assessment about attribute weightings and the appropriateness of alternative versus selection attributes in order to obtain final scores which are called vague appropriateness indices. A numerical example is presented to show the practical applicability of this approach.

• The Journal of the Korea institute of electronic communication sciences
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• v.10 no.6
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• pp.683-690
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• 2015

In Rough set, attribute missing values have several problems such as reduct and core estimation. Further, they do not give some discernable pattern for decision tree construction. Now, there are several methods such as substitutions of typical attribute values, assignment of every possible value, event covering, C4.5 and special LEMS algorithm. However, they are mainly substitutions into frequently appearing values or common attribute ones. Thus, decision rules with high information loss are derived in case that important attribute values are missing in pattern matching. In particular, there is difficult to implement cross validation of the decision rules. In this paper we suggest new method for substituting the missing attribute values into high information gain by using entropy variation among given attributes, and thereby completing the information table. The suggested method is validated by conducting the same rough set analysis on the incomplete information system using the software ROSE.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.11 no.1
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• pp.171-176
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• 2011

An attribute grammar is a set of semantic rules added to the syntax rules. This attribute grammar uses two attributes. It is difficult to write by its additional rules to the existing syntax rules with two attributes understanding the parsing steps. So this paper analyses attributes and an attribute grammar to construct the attribute grammar easily proposing three definitions and shows a possibility that an attribute grammar can be written with only one attribute in some cases.

• The Journal of Economics, Marketing and Management
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• v.8 no.3
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• pp.9-21
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• 2020

Purpose: Previous literature shows that a price promotion serves as a negative cue of product quality especially when consumers have no additional information about the product's other attributes. In this research, we explore how the effect of price promotions on consumers' perceptions of product quality changes depending on their ability to compare promoted product attributes with competitive products' attributes. Research design, data and methodology: Specifically, we use a series of scenario-based lab experiments using different types of products and explore if attribute alignability among competing products in a consumer's choice set influences consumers' ability to compare the product attributes and perceived quality. Results: Our study findings show that high attribute alignability among products makes consumers easier to compare the product attributes and thereby focus more on non-price information than price information. We also show that attribute alignability serves as a moderator and decreases perceived quality when the promotion level is higher. Therefore, the attribute alignability weakens the negative impact of a price promotion on consumers' perceived product quality. Conclusions: Our study findings provide new insights on how to implement price promotion strategies while keeping products' perceived quality, by considering the product's relationships with competing products in a choice set.

• Journal of Korea Technology Innovation Society
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• v.2 no.2
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• pp.201-218
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• 1999

The article contains an introduction of possibility of applying Multi-Attribute Utility Theory(MAUT) for priority setting of R&D projects. MAUT is compared with AHP, which is widely used recently. These two techuiques are applied to set priorities of R&D projects In a Government-funded Research Institute. Six criteria are chosen from consultation with decision makers. They are composed of 1) validity as representative projects, 2) possibility of resource mobilization, 3) spillover effect of developed technologies, 4) possibility of success, 5) scope of participation and 6) clarity of research goal. To set priorities of R&D projects, SMART(Simple MultiAttribute Rating Technique) and DVM(Difference Value Measurement) out of many MAUT methods are used to design the utility function and to determine the weights among criteria. The aggregation model is additive on the assumption the criteria are independent. AHP executes pairwise comparisons for criteria and alternatives. From the results of the case study, the results and theoretical characteristics are compared.

• Journal of Information Processing Systems
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• v.16 no.2
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• pp.360-376
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• 2020

Many heuristic attribute reduction algorithms have been proposed to find a single reduct that functions as the entire set of original attributes without loss of classification capability; however, the proposed reducts are not always perfect for these multiclass datasets. In this study, based on a probabilistic rough set model, we propose the class-oriented attribute reduction (COAR) algorithm, which separately finds a reduct for each target class. Thus, there is a strong dependence between a reduct and its target class. Consequently, we propose a type of ensemble constructed on a group of classifiers based on class-oriented reducts with a customized weighted majority voting strategy. We evaluated the performance of our proposed algorithm based on five real multiclass datasets. Experimental results confirm the superiority of the proposed method in terms of four general evaluation metrics.

• Journal of KIISE:Software and Applications
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• v.30 no.7_8
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• pp.764-772
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• 2003

The main thrust of our talk will be based on our experience in developing and applying an attribute-based classification technique in the context of an operational answer set driven retrieval system. To alleviate the difficulty and reduce the cost of manually constructing and maintaining answer sets, i.e., knowledge base, we have devised a new method of automating the answer document selection process by using the notion of attribute-based classification, which is in and of itself novel. We attempt to explain through experiments how helpful the proposed method is for the knowledge base construction process.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.28 no.1
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• pp.1-7
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• 2005

Data mining is widely used for turning huge amounts of data into useful information and knowledge in the information industry in recent years. When analyzing data set with continuous values in order to gain knowledge utilizing data mining, we often undergo a process called discretization, which divides the attribute's value into intervals. Such intervals from new values for the attribute allow to reduce the size of the data set. In addition, discretization based on rough set theory has the advantage of being easily applied. In this paper, we suggest a discretization algorithm based on Rough Set theory and SOM(Self-Organizing Map) as a means of extracting valuable information from large data set, which can be employed even in the case where there lacks of professional knowledge for the field.