• Journal of Korea Multimedia Society
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• v.17 no.7
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• pp.769-777
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• 2014

With the rapid interest in Geographic Information System (GIS) contents, a large volume of valuable GIS dataset has been distributed illegally by pirates, hackers, or unauthorized users. Therefore the problem focus on how to protect the copyright of GIS vector map data for storage and transmission. At this point, GIS security techniques focusing on secure network and data encryption have been studied and developed to solve the copyright protection and illegal copy prevention for GIS digital map. But GIS vector map data is very large and current data encryption techniques often encrypt all components of data. That means we have encrypted large amount of data lead to the long encrypting time and high complexity computation. This paper presents a novel selective encryption scheme for GIS vector map data protection to store, transmit or distribute to authorized users using K-means algorithm. The proposed algorithm only encrypts a small part of data based on properties of polylines and polygons in GIS vector map but it can change whole data of GIS vector map. Experimental results verified the proposed algorithm effectively and error in decryption is approximately zero.

• Journal of the Korean Data and Information Science Society
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• v.16 no.2
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• pp.411-418
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• 2005

We review support vector and wavelet density estimation. The relationship between support vector and wavelet density estimation in reproducing kernel Hilbert space (RKHS) is investigated in order to use wavelets as a variety of support vector kernels in support vector density estimation.

• Journal of Korea Multimedia Society
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• v.20 no.8
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• pp.1312-1320
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• 2017

Recently, vector map data is developed and used in many domains widely. In the most cases, vector map data contains confidential information which must be kept away from unauthorized users. Moreover, the production process of vector maps is considerably complex and consumes a lot of money and human resources. Therefore, the secured storage and transmission are necessary to prevent the illegal copying and distribution from hacker. This paper presents a selective encryption algorithm using geometric objects in frequency domain for vector map data. In the proposed algorithm, polyline and polygon data in vector map is the target of the selective encryption process. Experimental results verified that proposed algorithm is effectively and adaptive the requirements of security.

• Journal of the Korean Data and Information Science Society
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• v.25 no.2
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• pp.447-454
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• 2014

In many practical machine learning and data mining applications, unlabeled training examples are readily available but labeled ones are fairly expensive to obtain. Therefore semi-supervised learning algorithms have attracted much attentions. However, previous research mainly focuses on classication problems. In this paper, a semi-supervised regression method based on support vector regression (SVR) formulation that is proposed. The estimator is easily obtained via the dual formulation of the optimization problem. The experimental results with simulated and real data suggest superior performance of the our proposed method compared with standard SVR.

• Journal of Internet Computing and Services
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• v.6 no.3
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• pp.159-170
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• 2005

OC-SVM(One Class Support Vector Machine) avoids solving a full density estimation problem, and instead focuses on a simpler task, estimating quantiles of a data distribution, i.e. its support. OC-SVM seeks to estimate regions where most of data resides and represents the regions as a function of the support vectors, Although OC-SVM is powerful method for data description, it is difficult to incorporate human subjective importance into its estimation process, In order to integrate the importance of each point into the OC-SVM process, we propose a fuzzy version of OC-SVM. In FOC-SVM (Fuzzy One-Class Support Vector Machine), we do not equally treat data points and instead weight data points according to the importance measure of the corresponding objects. That is, we scale the kernel feature vector according to the importance measure of the object so that a kernel feature vector of a less important object should contribute less to the detection process of OC-SVM. We demonstrate the performance of our algorithm on several synthesized data sets, Experimental results showed the promising results.

• Journal of the Korea Society of Computer and Information
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• v.21 no.1
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• pp.107-113
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• 2016

In this paper, by managing the biometric data is changed with the passage of time, a systematic and scientifically propose a framework to increase the bio-vector generation efficiency of the smart health care. Increasing the development of human life as a medicine and has emerged smart health care according to this. Organic and efficient health management becomes possible to generate a vector when the biological domain to the wireless communication infrastructure based on the measurement of the health status and to take action in accordance with the change of the physical condition. In this paper, we propose a framework to create a bio-vector that contains information about the current state of health of the person. In the proposed framework, Bio vectors may be generated by collecting the biometric data such as blood pressure, pulse, body weight. Biometric data is the raw data from the bio-vector. The scope of the primary data can be set to active. As the collecting biometric data from multiple items of the bio-recognition vectors may increase. The resulting bio-vector is used as a measure to determine the current health of the person. Bio-vector generating the proposed framework, it can aid in the efficiency and systemic health of healthcare for the individual.

• Journal of the Korean Data and Information Science Society
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• v.24 no.3
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• pp.625-636
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• 2013

In this paper we study four kernel machines for estimating expected shortfall, which are constructed through combinations of support vector quantile regression (SVQR), restricted SVQR (RSVQR), least squares support vector machine (LS-SVM) and support vector expectile regression (SVER). These kernel machines have obvious advantages such that they achieve nonlinear model but they do not require the explicit form of nonlinear mapping function. Moreover they need no assumption about the underlying probability distribution of errors. Through numerical studies on two artificial an two real data sets we show their effectiveness on the estimation performance at various confidence levels.

• Spatial Information Research
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• v.17 no.1
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• pp.51-66
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• 2009

There have been concerned about the copyright as numerous data are digitalized because of the growth of performance of the computer and Internet. Digital watermarking is one of strong methods to protect copyright. We proposed a novel digital watermarking for vector map data. Although vector map data are used widely in GIS, there is little interest in copyright. The proposed method is to embed and extract watermarks using CRC principle. The experimental results show that this method can protect the copyright of the vector map by extracting embedded watermarks. Therefore, the proposed method can be utilized as the technique to protect vector map data.

• Journal of Korea Multimedia Society
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• v.18 no.7
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• pp.818-826
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• 2015

With the rapid interest in Geographic Information System (GIS) contents, a large volume of valuable GIS dataset has been distributed illegally by pirates, hackers, or unauthorized users. Therefore the problem focus on how to protect the copyright of GIS vector map data for storage and transmission. But GIS vector map data is very large and current data encryption techniques often encrypt all components of data. That means we have encrypted large amount of data lead to the long encrypting time and high complexity computation. This paper presents the selective encryption scheme using hybrid transform for GIS vector map data protection to store, transmit or distribute to authorized users. In proposed scheme, polylines and polygons in vector map are targets of selective encryption. We select the significant objects in polyline/polygon layer, and then they are encrypted by the key sets generated by using Chaotic map before changing them in DWT, DFT domain. Experimental results verified the proposed algorithm effectively and error in decryption is approximately zero.

• Spatial Information Research
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• v.15 no.4
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• pp.385-395
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• 2007

As the growth of performance of the computer and the development of the Internet are exponential, sharing and using the information illegally have also increased to the same proportion. In this paper, we proposed a novel method on the vector map data among digital contents. Vector map data are used for GIS, navigation and web-based services etc. We embedded watermark into the coordinate of the vector map data using bit operation and extracted the watermark. This method helps to protect the copyright of the vector map data. This watermarking method is a spatial domain method and it embeds the watermark within an allowable error. Our experiment shows that the watermark produced by this method is resistant to simplification and translation.