  Diferentially private (DP) synthetic datasets are a powerful approach for
training machine learning models while respecting the privacy of individual
data providers. The effect of DP on the fairness of the resulting trained
models is not yet well understood. In this contribution, we systematically
study the effects of differentially private synthetic data generation on
classification. We analyze disparities in model utility and bias caused by the
synthetic dataset, measured through algorithmic fairness metrics. Our first set
of results show that although there seems to be a clear negative correlation
between privacy and utility (the more private, the less accurate) across all
data synthesizers we evaluated, more privacy does not necessarily imply more
bias. Additionally, we assess the effects of utilizing synthetic datasets for
model training and model evaluation. We show that results obtained on synthetic
data can misestimate the actual model performance when it is deployed on real
data. We hence advocate on the need for defining proper testing protocols in
scenarios where differentially private synthetic datasets are utilized for
model training and evaluation.
