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Privacy-Preserving Data Publishing: Research on Trends in De-identification Techniques for Structured and Unstructured Data
Yongki Hong, Gihyuk Ko, Heedong Yang, Seung Hwan Ryu
http://doi.org/10.5626/JOK.2023.50.11.1008
The advent of AI has seen an increased demand for data for AI development, leading to a proliferation of data sharing and distribution. However, there is also the risk of personal information disclosure during data utilization and therefore, it is necessary to undergo a process of de-identification before distributing the data. Privacy-Preserving Data Publishing (PPDP) is a series of procedures aimed at adhering to specified privacy guidelines while maximizing the utility of data. It has been continuously researched and developed. Since the early 2000s, techniques for de-identifying structured data (e.g., tables or relational data) were studied. As a significant portion of the collected data is now unstructured data and its proportion is increasing, research on de-identification techniques for unstructured data is also actively being conducted. In this paper, we aim to introduce the existing de-identification techniques for structured data and discuss recent trends in de-identification techniques for unstructured data.
RDID-GAN: Reconstructing a De-identified Image Dataset to Generate Effective Learning Data
Wonseok Oh, Kangmin Bae, Yuseok Bae
http://doi.org/10.5626/JOK.2021.48.12.1329
Recently, CCTVs have been installed to prevent or handle various social problems, and there are many efforts to develop visual surveillance systems based on deep neural networks. However, the datasets collected from CCTVs are inappropriate to train models due to privacy issues. Therefore, in this paper, we proposed RDID-GAN, an effective dataset de-identification method that can remove privacy issues and negative effects raised by modifying the dataset using a de-identification procedure. RDID-GAN focuses on a de-identified region to produce competitive results by adopting the attention module. Through the experiments, we compared RDID-GAN and the conventional image-to-image translation models qualitatively and quantitatively.
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.
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