  Machine learning is increasingly used in the most diverse applications and
domains, whether in healthcare, to predict pathologies, or in the financial
sector to detect fraud. One of the linchpins for efficiency and accuracy in
machine learning is data utility. However, when it contains personal
information, full access may be restricted due to laws and regulations aiming
to protect individuals' privacy. Therefore, data owners must ensure that any
data shared guarantees such privacy. Removal or transformation of private
information (de-identification) are among the most common techniques.
Intuitively, one can anticipate that reducing detail or distorting information
would result in losses for model predictive performance. However, previous work
concerning classification tasks using de-identified data generally demonstrates
that predictive performance can be preserved in specific applications. In this
paper, we aim to evaluate the existence of a trade-off between data privacy and
predictive performance in classification tasks. We leverage a large set of
privacy-preserving techniques and learning algorithms to provide an assessment
of re-identification ability and the impact of transformed variants on
predictive performance. Unlike previous literature, we confirm that the higher
the level of privacy (lower re-identification risk), the higher the impact on
predictive performance, pointing towards clear evidence of a trade-off.
