# Evaluating Cluster-Based Synthetic Data Generation for Blood-Transfusion Analysis
## 4 Discussion
This allowed for a tractable privacy analysis that evaluates which relations have been learnt by the model. The privacy analyses demonstrated that the specified attack scenario would not be successful for continuous variables. For categorical variables, however, attribute information could be inferred by an attacker with sufficient background information. An additional analysis with donors who were not part of the training set yielded similar results for these variables (Deferral Status and Sex). In other words, being part of the training data for synthetic data generation was not associated with a significantly increased risk of leaking sensitive information for categorical variables. This demonstrates that opportunities for attribute linkage can be explained (at least in part) by properties of the data and do not necessarily result from overfitting by CBSDG. One such property could be the imbalance in Deferral Status, where guessing a negative deferral status is already accurate for 98% of the donors (even for attackers without background information). Furthermore, several variables were highly correlated, e.g., it is well-known that there is a strong relation between sex and ferritin level [35]. Men have a higher ferritin level on average and information on this variable alone could be enough to infer a donor's sex. This also relates to the low expected deviation for Previous Hb, where an attacker's guess on a donor's Hb will be off by 'only' 0.69 mmol/L. Apart from data characteristics there remain other factors that can lead to differences between privacy risks for categorical and continuous values. Categorical variables generally take on fewer unique values, which already increases the probability of correct inference. In addition, with CBSDG, histograms are used to model the continuous distributions within clusters, through which different values are grouped together in histogram bins. This is a form of _generalization_; an anonymization technique used to increase privacy [24]. For categorical variables, the probability of each value is described separately and privacy is not increased with generalization. In the privacy analyses, it was assumed that adversaries had information on all eight remaining variables to mimic a strong attacker. This scenario was selected in order to generate generalizable analysis results that can be related to situations with less powerful attackers as well. On the other hand, this may have resulted in a scenario with an unrealistically knowledgeable attacker. For example, donors generally do not know their exact ferritin value, and therefore an attacker is unlikely to have this background information as well. Nevertheless, there may be a desire to further increase privacy. One solution could be to modify the clustering, such that more dissimilar individuals are combined. Future research is needed to determine how this impacts utility, as clustering more dissimilar individuals may decrease the extent to which correlations are represented. Explicit parameterization of this property (i.e., the similarity of individuals within clusters) would capacitate users in navigating the privacy-utility trade-off more effectively. Furthermore, there are attack scenarios that are not covered by the analysis (such as membership inference attacks) which require further investigation. Compared to the simulations previously performed by [PERSON] et al. (2023) [22], an important additional step for practical implementation is data selection. Deciding on which version of the data will be synthesized can be an impactful choice, which may require either experience or guidance by an expert. Essentially, the data set goes through different forms during pre-processing and these can differ in the extent to which they are suitable for synthetic data generation. In general, we recommend synthetizing as few variables as possible, considering that the number of inter-variable associations increase factorially with the number of variables.