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.
Abstract (translated)
保护用户数据的隐私对高品质体验（QoE）和可接受性至关重要，特别是对于处理敏感数据的服务的QoE。然而，显示匿名化技术易被数据重新识别，而合成数据生成技术逐渐取代了匿名化，因为它相对较短的时间和资源消耗，且对数据泄漏更健壮。生成对抗网络（GANs）被用于生成合成数据，尤其是遵循差分隐私现象的GAN框架。这项研究将最先进的基于GAN的合成数据生成模型与用于生成阿尔茨海默病患者的时序合成医疗记录的隐私保护进行了比较，这些数据在没有隐私担忧的情况下可以自由分发。预测建模、自相关和分布分析用于评估生成数据的生成质量（QoG）。通过应用成员推断攻击来确定潜在的数据泄漏风险，评估了所提出的模型的隐私保护能力。我们的实验结果表明，在保持可接受隐私水平的同时，隐私保护的GAN（PPGAN）模型比其他模型在隐私保护方面具有优势。所呈现的结果可以为未来医疗使用场景提供更好的数据保护支持。
URL
https://arxiv.org/abs/2402.14042
