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A Study on the Prediction Accuracy of Machine Learning using De-Identified Personal Information
Hongju Jung, Nayoung Lee, Soo-jin Seol, Kyeong-Seok Han
http://doi.org/10.5626/JOK.2020.47.10.906
The de-identification of personal information is emerging due to the revision of the Personal Information Protection and Personal Information Protection Act. In addition, the use of artificial intelligence and machine learning is becoming a driving force in the Fourth Industrial Revolution. In this paper, we experimentally verify the predictive accuracy of a machine learning decision tree algorithm using de-identified personal information by applying k-anonymity (k=2). The prediction results of the input data are compared to determine the limitations of using de-identified personal information in machine learning. According to the amendment of the Personal Information Protection Act, we propose that when using de-identified personal information in machine learning, the level of personal information de-identification and the analysis algorithm should be considered.
Re-anonymization Technique for Dynamic Data Using Decision Tree Based Machine Learning
Young Ki Kim, Choong Seon Hong
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.
A Study on Service-based Secure Anonymization for Data Utility Enhancement
Chikwang Hwang, Jongwon Choe, Choong Seon Hong
Personal information includes information about a living human individual. It is the information identifiable through name, resident registration number, and image, etc. Personal information which is collected by institutions can be wrongfully used, because it contains confidential information of an information object. In order to prevent this, a method is used to remove personal identification elements before distributing and sharing the data. However, even when the identifier such as the name and the resident registration number is removed or changed, personal information can be exposed in the case of a linking attack. This paper proposes a new anonymization technique to enhance data utility. To achieve this, attributes that are utilized in service tend to anonymize at a low level. In addition, the anonymization technique of the proposal can provide two or more anonymized data tables from one original data table without concern about a linking attack. We also verify our proposal by using the cooperative game theory.
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