학술논문

Protect and Extend - Using GANs for Synthetic Data Generation of Time-Series Medical Records
Document Type
Conference
Source
2023 15th International Conference on Quality of Multimedia Experience (QoMEX) Quality of Multimedia Experience (QoMEX), 2023 15th International Conference on. :171-176 Jun, 2023
Subject
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
General Topics for Engineers
Signal Processing and Analysis
Data privacy
Privacy
Predictive models
Generative adversarial networks
Data models
Information filtering
Quality of experience
Synthetic data generation
differential privacy
medical data privacy
GAN-based models
Language
ISSN
2472-7814
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 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.