Protect and Extend - Using GANs for Synthetic Data Generation of Time-Series Medical Records

Abstract

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 ITbased 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.

Machine learning algorithms for detection of visuomotor neural control differences in individuals with PASC and ME

“…On a related note, recent studies highlight the value of synthetic data for safeguarding privacy, particularly in healthcare applications. By applying GANs (Goodfellow et al, 2014 ), one such study proved that synthetic medical records are not just high-quality but also offer formidable protection against potential data leaks (Ashrafi et al, 2023 ). In this study, we describe the implementation of machine learning and deep learning models to distinguish brain wave activity collected from individuals affected by PASC or ME from healthy control participants during the performance of a challenging rule-based visuomotor skill task.…”