• Journal of the Korea Society of Computer and Information
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• v.22 no.12
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• pp.101-108
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• 2017

The study proposed a system that filters the data that is entered when analyzing big data such as SNS and BLOG. Personal information includes impersonal personal information, but there is also personal information that distinguishes it from personal information, such as religious institution, personal feelings, thoughts, or beliefs. Define these personally identifiable information as sensitive information. In order to prevent this, Article 23 of the Privacy Act has clauses on the collection and utilization of the information. The proposed system structure is divided into two stages, including Big Data Processing Processes and Sensitive Information Filtering Processes, and Big Data processing is analyzed and applied in Big Data collection in four stages. Big Data Processing Processes include data collection and storage, vocabulary analysis and parsing and semantics. Sensitive Information Filtering Processes includes sensitive information questionnaires, establishing sensitive information DB, qualifying information, filtering sensitive information, and reliability analysis. As a result, the number of Big Data performed in the experiment was carried out at 84.13%, until 7553 of 8978 was produced to create the Ontology Generation. There is considerable significan ce to the point that Performing a sensitive information cut phase was carried out by 98%.

• International Journal of Internet, Broadcasting and Communication
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• v.13 no.1
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• pp.82-89
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• 2021

Recently, applications are increasing the importance of security for published documents. This paper deals with data-publishing where the publishers must state sensitive information that they need to protect. If a document containing such sensitive information is accidentally posted, users can use common-sense reasoning to infer unauthorized information. In recent studied of peer-to-peer databases, studies on the security of data of various unique groups are conducted. In this paper, we propose a security framework that fundamentally blocks user inference about sensitive information that may be leaked by XML constraints and prevents sensitive information from leaking from general user. The proposed framework protects sensitive information disclosed through encryption technology. Moreover, the proposed framework is query view security without any three types of XML constraints. As a result of the experiment, the proposed framework has mathematically proved a way to prevent leakage of user information through data inference more than the existing method.

• Journal of Web Engineering
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• v.21 no.4
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• pp.1265-1282
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• 2022

The theme, background, and signal music usage history information consists of general information such as music information, platform information, and music usage information, and sensitive information such as rights management information, music usage permission range, and contract information. If sensitive information among these is disclosed, disputes such as trade secrets and infringement of personal information protection between companies or between companies and individuals may arise. We propose a selective encryption/decryption method to secure the confidentiality, integrity, reliability and non-repudiation of sensitive music usage history information used in the theme, background, and signal music blockchain environment. In the proposed method, a monitoring company encrypts sensitive information using a secret key for usage history information, which is combined with general information, and digitally signs it using a private key to register it in a block. A trust group can view and access the information at the time of inquiry by verifying the digital signature with the public key of the monitoring company and then can decrypt the sensitive information using the private key.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.5
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• pp.1137-1143
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• 2015

Sensitive internal information has been transmitted in a variety of services of Internet environment, but almost users do not know what internal information is sent. In this paper, we intend to develop a new filtering system that continuously monitors the sensitive information in outbound network packets and notifies the internal user whether or not to expose. So we design a filtering system for sensitive information and analyze the implementation results. Thus users visually can check whether disclosure of the important information and drop the corresponding packets by the proposed system. The results of this study can help decrease cyber threats various targeting internal information of company by contributing to prevent exposure of sensitive internal information.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.5
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• pp.1433-1449
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• 2023

In recent years, the rapid development of social networks has led to a rapid increase in the amount of information available on the Internet, which contains a large amount of sensitive information related to pornography, politics, and terrorism. In the aspect of sensitive image detection, the existing machine learning algorithms are confronted with problems such as large model size, long training time, and slow detection speed when auditing and supervising. In order to detect sensitive images more accurately and quickly, this paper proposes a multiclassification sensitive image detection method based on lightweight Convolutional Neural Network. On the basis of the EfficientNet model, this method combines the Ghost Module idea of the GhostNet model and adds the SE channel attention mechanism in the Ghost Module for feature extraction training. The experimental results on the sensitive image data set constructed in this paper show that the accuracy of the proposed method in sensitive information detection is 94.46% higher than that of the similar methods. Then, the model is pruned through an ablation experiment, and the activation function is replaced by Hard-Swish, which reduces the parameters of the original model by 54.67%. Under the condition of ensuring accuracy, the detection time of a single image is reduced from 8.88ms to 6.37ms. The results of the experiment demonstrate that the method put forward has successfully enhanced the precision of identifying multi-class sensitive images, significantly decreased the number of parameters in the model, and achieved higher accuracy than comparable algorithms while using a more lightweight model design.

• KIPS Transactions on Computer and Communication Systems
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• v.5 no.9
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• pp.217-222
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• 2016

Recent research on integrated and peer-to-peer databases has produced new methods for handling various types of shared-group and process data. This paper with data publishing, where the publisher needs to specify certain sensitive information that should be protected. The proposed method cannot infer the user's sensitive information is leaked by XML constraints. In addition, the proposed secure framework uses encrypt to prevent the leakage of sensitive information from authorized users. In this framework, each node of sensitive data in an eXtensible Markup Language (XML) document is encrypted separately. All of the encrypted data are moved from their original document, and are bundled with an encrypted structure index. Our experiments show that the proposed framework prevents information being leaked via data inference.

• Journal of Korea Multimedia Society
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• v.24 no.10
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• pp.1391-1402
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• 2021

This paper focuses use context-sensitive spelling error correction using generative adversarial network. Generative adversarial network[1] are attracting attention as they solve data generation problems that have been a challenge in the field of deep learning. In this paper, sentences are generated using word embedding information and reflected in word distribution representation. We experiment with DCGAN[2] used for the stability of learning in the existing image processing and D2GAN[3] with double discriminator. In this paper, we experimented with how the composition of generative adversarial networks and the change of learning corpus influence the context-sensitive spelling error correction In the experiment, we correction the generated word embedding information and compare the performance with the actual word embedding information.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.2
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• pp.540-557
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• 2021

Privacy vulnerability of social networks is one of the major concerns for social science research and business analysis. Most existing studies which mainly focus on un-weighted network graph, have designed various privacy models similar to k-anonymity to prevent data disclosure of vertex attributes or relationships, but they may be suffered from serious problems of huge information loss and significant modification of key properties of the network structure. Furthermore, there still lacks further considerations of privacy protection for important sensitive edges in weighted social networks. To address this problem, this paper proposes a privacy preserving method to protect sensitive weighted edges. Firstly, the sensitive edges are differentiated from weighted edges according to the edge betweenness centrality, which evaluates the importance of entities in social network. Then, the perturbation operations are used to preserve the privacy of weighted social network by adding some pseudo-edges or modifying specific edge weights, so that the bottleneck problem of information flow can be well resolved in key area of the social network. Experimental results show that the proposed method can not only effectively preserve the sensitive edges with lower computation cost, but also maintain the stability of the network structures. Further, the capability of defending against malicious attacks to important sensitive edges has been greatly improved.

• Journal of the Korean Data and Information Science Society
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• v.13 no.1
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• pp.35-45
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• 2002

For improving the quality of survey dat a of sensitive character, we suggest a conditional in direct survey method. In th at method, only the respondents who answer directly to the less sensitive question respond indirectly to the more sensitive one by using the one sample unrelated question randomized response technique with the known $\pi_y$, the true proportion of unrelated group Y. We extend it to two sample method when $\pi_y$ is unknown. We also consider the case that people who possess less sensitive character answer untruthfully. Finally we compare our method with the methods of Greenberg et al. and Carr et al..

• Journal of the Korean Data and Information Science Society
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• v.15 no.4
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• pp.731-741
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• 2004

This paper is planned to use multi-proportions randomized response model for sensitive information on internet survey. This is an indirect response technique as a way of obtaining much more precise information. In this system we consider that respondents are generally reluctant to answer in a survey to get sensitive information targeting employees, customers, etc.