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

arXiv:2402.14042v1 Announce Type: cross
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 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 …

anonymization anonymization techniques arxiv cs.ai cs.cr cs.lg data experience gans health health services high identification medical paramount preservation private protect qoe quality quality of experience records re-identification sensitive sensitive data series services synthetic synthetic data techniques user data