  Big data analysis poses the dual problem of privacy preservation and utility,
i.e., how accurate data analyses remain after transforming original data in
order to protect the privacy of the individuals that the data is about - and
whether they are accurate enough to be meaningful. In this paper, we thus
investigate across several datasets whether different methods of generating
fully synthetic data vary in their utility a priori (when the specific analyses
to be performed on the data are not known yet), how closely their results
conform to analyses on original data a posteriori, and whether these two
effects are correlated. We find some methods (decision-tree based) to perform
better than others across the board, sizeable effects of some choices of
imputation parameters (notably the number of released datasets), no correlation
between broad utility metrics and analysis accuracy, and varying correlations
for narrow metrics. We did get promising findings for classification tasks when
using synthetic data for training machine learning models, which we consider
worth exploring further also in terms of mitigating privacy attacks against ML
models such as membership inference and model inversion.
