# Privacy and Utility of Private Synthetic Data for Medical Data Analyses
## 2 Related Work
The authors benchmarked four different GAN-based algorithms for generating synthetic data against common machine learning metrics. In addition, the authors introduced an ensemble method called QUAIL, which can be embedded in other DP-GANs and enables an even distribution of the privacy budget. The benchmark showed that the combination with QUAIL outperforms the baseline DP generators. The result is like others, with no clear winner, but PATE-CTGAN often has a better performance due to higher utility and statistical similarity when using higher \(\epsilon\) values. Unlike our work, [PERSON] et al. measured precision and utility on various datasets without a concrete domain-specific use case. Additionally, the focus is on assessing the utility rather than the degree of privacy of the data. A more traditional concept was pursued for the DataSynthesizer project by [PERSON] et al. [12]. Here, a three-component workflow is used to create synthetic data based on tabular input data. At first, the DataDescriber reads the structure, correlation, and distribution of the data. From this, the DataGenerator creates a synthetic dataset using the data points. The ModelInspector analyzes this dataset and provides parameters to iteratively refine the data quality of the synthetic dataset. In contrast to other technologies, DataSynthesizer does not use a GAN approach or a DP mechanism. Although the concept appears to be interesting, we exclude it from our work as it seems too far from the scope of the technologies used here. A paper that proves the real-world feasibility of synthetic data is \"Differentially Private Medical Texts Generation Using Generative Neural Networks\" [13]. [PERSON] et al. used a differentially private training method in combination with the Generative Pre-trained Transformer 2 (GPT-2) language generation model, where Gaussian noise is added on the weights in every iteration of the training process. To evaluate their approach, the authors used several metrics to compare the generated data to the reference data set. From a utility perspective, the approach received good scores, but the authors remarked that they did not consult medical experts to verify the plausibility of the medical data. In addition, they also noted that they did not de-identify the data or include anything to prevent the model from learning identifying data. This also underlines our research question to pay more attention to use case-specific attacks on privacy. Nevertheless, the paper shows that synthetic data are a promising tool for the medical domain. In another work by [PERSON] et al. [14], the issue of data correlations and preserved patterns that could seriously undermine privacy is examined. An exposure metric is introduced, which measures the characteristics of the dataset. Using this metric, the problem of unintended memorization is demonstrated on some sample data sets. The authors recommend strategies such as regularization, sanitization, as well as DP to mitigate those issues. However, these strategies cannot be applied in general, and our approach focuses on techniques that already use DP. The just presented small share of all papers that address the topic of synthetic private data generation shows the broad interest of the community in this topic. However, we found no work that conducts an evaluation like ours by choosing a real-world use case, defining a scenario-specific privacy definition, and benchmarking different technologies for that use case.