A Self-Attention Synthesizing Model with Privacy-Preserving(ACCT-GAN) for Medical Tabular Data

Accurate clinical cancer prediction is important for clinical diagnosis. Most of the cancer patient data have high privacy and using clinical patient data directly may create concerns such as privacy leakage, which can be solved by replacing real data with synthetic data for clinical trial modelling. Synthetic data can also fill in the missing, unbalanced clinical datasets. Data augmentation can achieve data synthesis. However, traditional data augmentation models are mostly based on convolutional neural network architectures, which can only capture local dependencies and do not well describe the feature-related information of patients over long survival times. In this paper, we propose a self-attention medical tabular data augmentation model with privacy protection, ACCT-GAN, based on the generative adversarial network model, which uses the AC block generated by combining the ACmix model to reconstruct the discriminator model in the CTAB-GAN+ network, which can effectively solve the limitations of the original convolutional neural network, model the global relationship, and make the generated data retain the feature correlation between the original data better correlations between the original data. Meanwhile, taking into account the privacy of medical data, the DP-SGD algorithm of CTAB-GAN+ is optimised to reduce the privacy risk of synthetic data. The results show that ACCT-GAN synthesizes privacy-preserving data with at least 26.21% higher utility across the dateset of nasopharyngeal cancer patients provided by Xiangya Medical College and learning tasks under different privacy budgets, demonstrating the usability, high quality, and better privacy preservation of the generated data, validating the effectiveness of this paper’s method, and confirming the potential of this paper’s method for cancer datasets.