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There is a constant trade-off between the utility of the data collected andprocessed by the many systems forming the Internet of Things (IoT) revolutionand the privacy concerns of the users living in the spaces hosting thesesensors. Privacy models, such as the SITA (Spatial, Identity, Temporal, andActivity) model, can help address this trade-off. In this paper, we focus onthe problem of $CO_2$ prediction, which is crucial for health monitoring butcan 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 tosimulate different SITA configurations on the collected data. We use thetransformed data with multiple Machine Learning (ML) techniques to analyse theperformance of the models to predict $CO_{2}$ levels. Our results show that,for different algorithms, different SITA configurations do not make onealgorithm 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 transformeddata. The results can be useful to show the effect of different levels of dataprivacy on the data utility of IoT applications, and can also help to identifywhich parameters are more relevant for those systems so that higher privacysettings can be adopted while data utility is still preserved.

Impact of using a privacy model on smart buildings data for CO2 prediction