• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2007.02a
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• pp.116-119
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• 2007

In a keyword search scheme a user stores encrypted data on an untrusted server and gives a database manager a capability for a keyword which enables a database manager to find encrypted data containing the keyword without revealing the keyword to the database manager. Conjunctive keyword search scheme enables a user to obtain data containing all of several keywords through only one query. One of the security requirements of conjunctive keyword search schemes is that a malicious adversary should not be able to generate new valid capabilities from the observed capabilities. In U:5 paper we show that conjunctive keyword search schemes are not secure. In particular, given two capabilities corresponding two sets of keywords, an adversary is able to generate a new capability corresponding to the dierence set of two keywords sets.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.5
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• pp.2718-2731
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• 2019

Conjunctive keyword search encryption is an important technique for protecting sensitive data that is outsourced to cloud servers. However, the process of searching outsourced data may facilitate the leakage of sensitive data. Thus, an efficient data search approach with high security is critical. To solve this problem, an efficient conjunctive keyword search scheme based on ciphertext-policy attribute-based encryption is proposed for cloud storage environment. This paper proposes an efficient mechanism for removing the secure channel and resisting off-line keyword-guessing attacks. The storage overhead and the computational complexity are regardless of the number of keywords. This scheme is proved adaptively secure based on the decisional bilinear Diffie-Hellman assumption in the standard model. Finally, the results of theoretical analysis and experimental simulation show that the proposed scheme has advantages in security, storage overhead and efficiency, and it is more suitable for practical applications.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• v.9 no.1
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• pp.1063-1066
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• 2005

Most reading systems that supply various study data generally support keyword search. But the usual keyword matching techniques have a problem to require the exact keyword matching, and could not find similar field materials. Futhermore, testing materials have too little information to apply the keyword matching search. To solve these problems, this thesis proposes the method to extract the important keyword from study data and to construct the database automatically when the data are stored at the storage. And using prepared similar terminology database, we suggest the intelligent and efficient technique to find study materials.

• Journal of KIISE:Databases
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• v.35 no.6
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• pp.495-504
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• 2008

Recently, there has been much work on supporting keyword search not only for text documents, but a]so for structured data such as relational data, XML data, and RDF data. In this paper, we propose an efficient keyword search method for RDF data. The proposed method first groups related nodes and edges in RDF data graphs to reduce data sizes for efficient keyword search and to allow relevant information to be returned together in the query answers. The proposed method also utilizes the semantics in RDF data to measure the relevancy of nodes and edges with respect to keywords for search result ranking. The experimental results based on real RDF data show that the proposed method reduces RDF data about in half and is at most 5 times faster than the previous methods.

• The KIPS Transactions:PartD
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• v.16D no.6
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• pp.859-870
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• 2009

Despite the surge in the research for keyword search method over relational database, only little attention has been devoted to studying on relational data stream.The research for keyword search over relational data stream is intense interest because streaming data is recently a major research topic of growing interest in the data management. In this regard we first analyze the researches related to keyword search methodover relational data stream, and then this paper focuses on the method of minimizing the join cost occurred while processing keyword search queries. As a result, we propose an advanced keyword search method that can yield more meaningful results for users on relational data streams. We also propose a query optimization method using layered-clustering for efficient query processing.

• Journal of Digital Convergence
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• v.18 no.3
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• pp.179-187
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• 2020

In a social environment where population aging is rapidly progressing, the healthcare service market is growing fast with the increasing interest in health and quality of life based on rising income levels and the evolution of technology. In this study, after keywords were extracted from Korean and US patent data published on KIPRIS from 2000 to October 2019, frequency analysis, time series analysis, and keyword network analysis were performed. Through this, the change of technology trends were identified, which keywords related to healthcare was shifted from traditional medical words to ICT words. In addition, although the keywords in Korean patents are 55% similar to those in the US, they show an absolute gap in patent production volume. In the next study, we will analyze various data such as domestic and international research and can obtain meaningful implications in the global market on the identified keywords.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.11
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• pp.5671-5693
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• 2017

With the rapid development of cloud computing, more and more data owners are motivated to outsource their data to cloud for various benefits. Due to serious privacy concerns, sensitive data should be encrypted before being outsourced to the cloud. However, this results that effective data utilization becomes a very challenging task, such as keyword search over ciphertexts. Although many searchable encryption methods have been proposed, they only support exact keyword search. Thus, misspelled keywords in the query will result in wrong or no matching. Very recently, a few methods extends the search capability to fuzzy keyword search. Some of them may result in inaccurate search results. The other methods need very large indexes which inevitably lead to low search efficiency. Additionally, the above fuzzy keyword search methods do not support access control. In our paper, we propose a searchable encryption method which achieves fuzzy search and access control through algorithm design and Ciphertext-Policy Attribute-based Encryption (CP-ABE). In our method, the index is small and the search results are accurate. We present word pattern which can be used to balance the search efficiency and privacy. Finally, we conduct extensive experiments and analyze the security of the proposed method.

• Journal of the Korea Convergence Society
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• v.9 no.7
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• pp.17-24
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• 2018

One of the major issues in big data processing is extracting keywords from internet and using them to process the necessary information. Most of the proposed keyword extraction algorithms extract keywords using search function of a large portal site. In addition, these methods extract keywords based on already posted or created documents or fixed contents. In this paper, we propose a KAES(Keyword Advertisement Extraction System) system that helps the potential shopping keyword marketing to extract issue keywords and related keywords based on dynamic instant messages such as various issues, interests, comments posted on SNS. The KAES system makes a list of specific accounts to extract keywords and related keywords that have most frequency in the SNS.

• The Journal of the Korea Contents Association
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• v.20 no.6
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• pp.1-8
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• 2020

The use of text data in big data analytics has been increased. So, much research on methods for text data analysis has been performed. In this paper, we study Bayesian learning based on conjugate prior for analyzing keyword data extracted from text big data. Bayesian statistics provides learning process for updating parameters when new data is added to existing data. This is an efficient process in big data environment, because a large amount of data is created and added over time in big data platform. In order to show the performance and applicability of proposed method, we carry out a case study by analyzing the keyword data from real patent document data.

• Journal of the Korean Operations Research and Management Science Society
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• v.38 no.1
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• pp.123-138
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• 2013

Keyword advertising has been used as a promotion tool rather than the advertising itself to online retailers. This is because the online retailer expects the direct sales increase when they deploy the keyword sponsorship. In practice, many online sellers rely on keyword advertising to promote their sales in short term with limited budget. Most of the previous researches use direct revenue factors as dependent variables such as CTR (click through rate) and CVI (conversion per impression) in their researches on the keyword advertising[14, 16, 22, 25, 31, 32]. Previous studies were, however, conducted in the context of aggregate-level due to the limitations on the data availability. These researches cannot evaluate the performance of keyword advertising in the individual level. To overcome these limitations, our research focuses on conversion of keyword advertising in individual-level. Also, we consider manageable factors as independent variables in terms of online retailers (the costs of keyword by implementation methods and meanings of keyword). In our study we developed the keyword advertising conversion model in the individual-level. With our model, we can make some theoretical findings and managerial implications. Practically, in the case of a fixed cost plan, an increase of the number of clicks is revealed as an effective way. However, higher average CPC is not significantly effective in increasing probability of purchase conversion. When this type (fixed cost plan) of implementation could not generate a lot of clicks, it cannot significantly increase the probability of purchase choice. Theoretically, we consider the promotional attributes which influence consumer purchase behavior and conduct individuals-level research based on the actual data. Limitations and future direction of the study are discussed.