  Preservation of private user data is of paramount importance for high Quality
of Experience (QoE) and acceptability, particularly with services treating
sensitive data, such as IT-based health services. Whereas anonymization
techniques were shown to be prone to data re-identification, synthetic data
generation has gradually replaced anonymization since it is relatively less
time and resource-consuming and more robust to data leakage. Generative
Adversarial Networks (GANs) have been used for generating synthetic datasets,
especially GAN frameworks adhering to the differential privacy phenomena. This
research compares state-of-the-art GAN-based models for synthetic data
generation to generate time-series synthetic medical records of dementia
patients which can be distributed without privacy concerns. Predictive
modeling, autocorrelation, and distribution analysis are used to assess the
Quality of Generating (QoG) of the generated data. The privacy preservation of
the respective models is assessed by applying membership inference attacks to
determine potential data leakage risks. Our experiments indicate the
superiority of the privacy-preserving GAN (PPGAN) model over other models
regarding privacy preservation while maintaining an acceptable level of QoG.
The presented results can support better data protection for medical use cases
in the future.
