• The Journal of Society for e-Business Studies
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• v.17 no.1
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• pp.137-150
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• 2012

For social demand and technological development, systematic private information management and security guidance have been enhanced; however, the issue of leakage and invasion of private information is shown in many ways. In the management of such private information, the issue of how to protect such information is one of the sensitive key elements. As a criterion to decide the management policy of each property information consisting of private information, this article suggests Dynamic-Security-Level-Measurement for property information. DSLM adopts the variable characteristics of property information as the element of measurement. By applying this method, it is possible to provide information management functions to cope with the changes of each property information security level of an individual actively. It is expected that this will improve the security of previous information management methods even more and also contribute to the improvement of security in integrated systems such as the integrated ID management system and electronic wallet.

• Journal of KIISE
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• v.44 no.1
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• pp.21-26
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• 2017

In recent years, new technologies such as Internet of Things, Cloud Computing and Big Data are being widely used. And the type and amount of data is dramatically increasing. This makes security an important issue. In terms of leakage of sensitive personal information. In order to protect confidential information, a method called anonymization is used to remove personal identification elements or to substitute the data to some symbols before distributing and sharing the data. However, the existing method performs anonymization by generalizing the level of quasi-identifier hierarchical. It requires a higher level of generalization in case where k-anonymity is not satisfied since records in data table are either added or removed. Loss of information is inevitable from the process, which is one of the factors hindering the utility of data. In this paper, we propose a novel anonymization technique using decision tree based machine learning to improve the utility of data by minimizing the loss of information.