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 2 R o C A l ). P 2 R o C A l offers better data utility than similar methods and the classification accuracies of P 2 R o C A l perturbed data streams are very close to those of the original data streams. P 2 R o C A l also provides higher resilience against data reconstruction attacks.