Abstract
In the era of big data, numerous data measurements collected from all walks of life are playing important roles in various data mining applications. Not all data owners (or keepers) could develop feasible learning models for knowledge discovery’s sake. Oftentimes, the original data need to be passed to or shared with researchers or data scientists for better mining insights, especially in the medical, financial, and industrial fields. However, concerns about sensitivity and privacy limit the availability and completeness of shared (or passed) data and the quality of mining results. In this paper, we propose a novel Convolutional Bidirectional Generative Adversarial Networks (CB-GAN) framework to generate sensitive synthetic data. The Convolutional Neural Networks are utilized to capture the feature correlations of the original data, and the Generative Adversarial Networks with Autoencoders are combined to synthesize realistically distributed data. To demonstrate the feasibility of the model, we evaluated it from three aspects: how similar are the distributions of the synthetic data to the original data, how well can the synthetic data accomplish future data mining tasks, and how much sensitive information has been hidden. Various experimental results showed the superiority of the proposed method compared with the state-of-the-art methods.
This research was supported by the Guangdong Natural Science Foundation General Program (Grant No. 2022A1515011713).
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Hu, R., Li, D., Ng, SK., Zheng, Z. (2023). CB-GAN: Generate Sensitive Data with a Convolutional Bidirectional Generative Adversarial Networks. In: Wang, X., et al. Database Systems for Advanced Applications. DASFAA 2023. Lecture Notes in Computer Science, vol 13946. Springer, Cham. https://doi.org/10.1007/978-3-031-30678-5_13
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