• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.9
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• pp.2550-2572
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• 2023

Epidemiological survey is an important means for the prevention and control of infectious diseases. Due to the particularity of the epidemic survey, 1) epidemiological survey in epidemic prevention and control has a wide range of people involved, a large number of data collected, strong requirements for information disclosure and high timeliness of data processing; 2) the epidemiological survey data need to be disclosed at different institutions and the use of data has different permission requirements. As a result, it easily causes personal privacy disclosure. Therefore, traditional access control technologies are unsuitable for the privacy protection of epidemiological survey data. In view of these situations, we propose a black box-assisted fine-grained hierarchical access control scheme for epidemiological survey data. Firstly, a black box-assisted multi-attribute authority management mechanism without a trusted center is established to avoid authority deception. Meanwhile, the establishment of a master key-free system not only reduces the storage load but also prevents the risk of master key disclosure. Secondly, a sensitivity classification method is proposed according to the confidentiality degree of the institution to which the data belong and the importance of the data properties to set fine-grained access permission. Thirdly, a hierarchical authorization algorithm combined with data sensitivity and hierarchical attribute-based encryption (ABE) technology is proposed to achieve hierarchical access control of epidemiological survey data. Efficiency analysis and experiments show that the scheme meets the security requirements of privacy protection and key management in epidemiological survey.

• Journal of Korean Society for Geospatial Information Science
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• v.6 no.2 s.12
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• pp.11-20
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• 1998

Although aerial photogrammetry has been used to generate or update digital maps. It is difficult to make the spatial and attribute data for all kinds of objects on the ground with only aerial photogrammetry. Therefore, we are getting informations of the object on the ground through an on-the-spot survey In order to improve accuracy and reliability of on-the-spot survey in this study, we obtained stereo images from high resolution digital camera (1152*864 pixels) and developed the video photogrammetry which was able to determine the three dimensional coordinates from stereo images by applying DLT(Direct Linear Transformation). Also, the developed video photogrammetry could generate and update the spatial and attribute data in digital maps by using a function that could connect three dimensional coordinates with the attribute data.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.3
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• pp.232-241
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• 2000

This study suggests an integrated method based on concept hierarchies, information gain, and rough set theory for efficient discovery rules from a large amount of data, and implements an optimal rules discovery system. Our approach applies attribute-oriented concept ascension technique to extract generalized knowledge from a database, knowledge reduction technique to remove superfluous attributes and attribute values, and significance of attributes to induce optimal rules. The system first reduces the size of database by removing the duplicate tuples through the condition attributes which have no influences on the decision attributes, and finally induces simplified optimal rules by removing the superfluous attribute values by analyzing the dependency relationships among the attributes. And we induce some decision rules from actual data by using the system and test rules to new data, and evaluate that the rules are well suited to them.

• Spatial Information Research
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• v.7 no.2
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• pp.271-282
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• 1999

In this paper, we derived an efficient indexing scheme, SJ tree, which handles multi-attribute data and spatial join operations efficiently. In addition, a number of algorithms for manipulating multi-attribute data are given , together with their computational and I/O complexity . Moreover , we how that SJ tree is a kind of generalized B-tree. This means that SJ-tree can be easily implemented on existing built-in B-tree in most storage managers in the sense that the structure of SJ tree is like that of B-tree. The spatial join operation with spatial output is benchmarked using R-tree, B-tree, K-D-B tree, and SJ tree. Results from the benchmark test indicate that SJ tree out performance other indexing schemes on spatial join with point data.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.2
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• pp.465-473
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• 2021

The main aim of this research paper is to identify the factors that influence smartphone purchase intention in the Nepalese market. The study identifies how the brand personality, attribute factors, and the price factor influence the purchase intention of a smartphone. The paper puts the emphasis on how the consumer preference functions in the selection of the smartphone and which factor plays the more significant role in smartphone purchase intention. This research paper has used primary data and a 7-point Likert scale survey questionnaire. The primary data has been collected through a structured survey questionnaire by using convenient sampling technique from 294 smartphone users in the Kathmandu Valley. Descriptive statistics, Correlation Analysis and Structural Equation Modeling (SEM) have been carried out to analyze the primary data using the SPSS AMOS 24. Brand personality, attribute factor, and product price were taken as independent variables to identify the impact on purchase intention. The result of the regression path analysis showed that brand personality has no significant effect on purchase intention in the purchasing of smartphone. It is also found that the product attributes and product price have a significant influence on consumer purchase intention of a smartphone in Nepal.

• International journal of advanced smart convergence
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• v.6 no.1
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• pp.39-43
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• 2017

As the industry grows, the amount of data grows exponentially, and data analysis using these serves as a predictable solution. As data size increases and processing speed increases, it has begun to be applied to new fields by combining artificial intelligence technology as well as simple big data analysis. In this paper, we propose a method to quickly apply a machine learning based algorithm through efficient resource allocation. The proposed algorithm allocates memory for each attribute. Learning Distinct of Attribute and allocating the right memory. In order to compare the performance of the proposed algorithm, we compared it with the existing K-means algorithm. As a result of measuring the execution time, the speed was improved.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.5
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• pp.2768-2780
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• 2019

Ciphertext-policy attribute-based encryption (CP-ABE) is one of the practical technologies to share data over cloud since it can protect data confidentiality and support fine-grained access control on the encrypted data. However, most of the previous schemes only focus on data confidentiality without considering data receiver privacy preserving. Recently, Li et al.(in TIIS, 10(7), 2016.7) proposed a CP-ABE with hidden access policy and testing, where they declare their scheme achieves privacy preserving for the encryptor and decryptor, and also has high decryption efficiency. Unfortunately, in this paper, we show that their scheme fails to achieve hidden access policy at first. It means that any adversary can obtain access policy information by a simple decisional Diffie-Hellman test (DDH-test) attack. Then we give a method to overcome this shortcoming. Security and performance analyses show that the proposed scheme not only achieves the privacy protection for users, but also has higher efficiency than the original one.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.12 no.2
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• pp.141-146
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• 1994

At the present time the cadastral sections has a document for constructing database of land register and commit to record the assessed cost of land in field. Kumjung-Ku, Pusan is a model in this study. It is possible to investigate the present land record by connecting graphic data with attribute data in author's program. AutoCAD make possible to connect graphic data with attribute data. Because of limitation of constructing database in AutoCAD, authors construct independent database in Clipper's circumstance. Database in AutoCAD and Clipper is connected to the menu-file in AutoCAD's circumstance.

• 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.

• Communications for Statistical Applications and Methods
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• v.30 no.6
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• pp.589-603
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• 2023

Deep generative models target to infer the underlying true data distribution, and it leads to a huge success in generating fake-but-realistic data. Regarding such a perspective, the data attributes can be a crucial factor in the data generation process since non-existent counterfactual samples can be generated by altering certain factors. For example, we can generate new portrait images by flipping the gender attribute or altering the hair color attributes. This paper proposes counterfactual disentangled variational autoencoder generative adversarial networks (CDVAE-GAN), specialized for data attribute level counterfactual data generation. The structure of the proposed CDVAE-GAN consists of variational autoencoders and generative adversarial networks. Specifically, we adopt a Gaussian variational autoencoder to extract low-dimensional disentangled data features and auxiliary Bernoulli latent variables to model the data attributes separately. Also, we utilize a generative adversarial network to generate data with high fidelity. By enjoying the benefits of the variational autoencoder with the additional Bernoulli latent variables and the generative adversarial network, the proposed CDVAE-GAN can control the data attributes, and it enables producing counterfactual data. Our experimental result on the CelebA dataset qualitatively shows that the generated samples from CDVAE-GAN are realistic. Also, the quantitative results support that the proposed model can produce data that can deceive other machine learning classifiers with the altered data attributes.