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.

通过比较各种使用生成对抗网络的合成数据生成模型，研究证明了隐私保护生成对抗网络（PPGAN）模型在保护隐私的同时保持了可接受的生成数据质量，为将来医疗应用提供更好的数据保护。

保护和扩展 —— 使用 GAN 生成时间序列医疗记录的合成数据

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

We describe a threat model under which a split network-based federated
learning system is susceptible to a model inversion attack by a malicious
computational server. We demonstrate that the attack can be successfully
performed with limited knowledge of the data distribution by the attacker. We
propose a simple additive noise method to defend against model inversion,
finding that the method can significantly reduce attack efficacy at an
acceptable accuracy trade-off on MNIST. Furthermore, we show that NoPeekNN, an
existing defensive method, protects different information from exposure,
suggesting that a combined defence is necessary to fully protect private user
data.

本文介绍了在分裂网络联合学习模型下，攻击者可以通过模型倒置的攻击手段窃取用户隐私数据，并提出了一种基于简单的加性噪声方法以保护用户数据隐私，同时本文通过对比分析显示，采用现有的方法并不能完全防御攻击，需要采用多种组合的防御策略。