  The widespread use of the Internet of Things (IoT) has raised many concerns,
including the protection of private information. Existing privacy preservation
methods cannot provide a good balance between data utility and privacy, and
also have problems with efficiency and scalability. This paper proposes an
efficient data stream perturbation method (named as $P^2RoCAl$). $P^2RoCAl$
offers better data utility than similar methods: classification accuracies of
$P^2RoCAl$ perturbed data streams are very close to those of the original data
streams. $P^2RoCAl$ also provides higher resilience against data reconstruction
attacks.
