The content discusses the importance of preserving private user data in healthcare services and the limitations imposed by regulations like GDPR. It compares anonymization techniques with synthetic data generation using GANs for better privacy protection. Various GAN-based models are evaluated for generating time-series synthetic medical records of dementia patients, emphasizing privacy preservation and data quality. The study includes predictive modeling, autocorrelation analysis, distribution assessment, and privacy evaluation through membership inference attacks. Results show that Privacy Preserving GAN (PPGAN) outperforms other models in balancing privacy preservation and data quality.
The research highlights the challenges in achieving high predictive model accuracy with limited medical data availability and emphasizes the need for further research to improve both Quality of Generating (QoG) and privacy aspects in synthetic data generation using GANs.
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