  There is a constant trade-off between the utility of the data collected and
processed by the many systems forming the Internet of Things (IoT) revolution
and the privacy concerns of the users living in the spaces hosting these
sensors. Privacy models, such as the SITA (Spatial, Identity, Temporal, and
Activity) model, can help address this trade-off. In this paper, we focus on
the problem of $CO_2$ prediction, which is crucial for health monitoring but
can be used to monitor occupancy, which might reveal some private information.
We apply a number of transformations on a real dataset from a Smart Building to
simulate different SITA configurations on the collected data. We use the
transformed data with multiple Machine Learning (ML) techniques to analyse the
performance of the models to predict $CO_{2}$ levels. Our results show that,
for different algorithms, different SITA configurations do not make one
algorithm perform better or worse than others, compared to the baseline data;
also, in our experiments, the temporal dimension was particularly sensitive,
with scores decreasing up to $18.9\%$ between the original and the transformed
data. The results can be useful to show the effect of different levels of data
privacy on the data utility of IoT applications, and can also help to identify
which parameters are more relevant for those systems so that higher privacy
settings can be adopted while data utility is still preserved.
