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 P2RoCAl). P2RoCAl offers better data utility than similar methods: classification accuracies of P2RoCAl perturbed data streams are very close to those of the original data streams. P2RoCAl also provides higher resilience against data reconstruction attacks.
@article{arxiv.1806.06151,
title = {Efficient Data Perturbation for Privacy Preserving and Accurate Data Stream Mining},
author = {M. A. P. Chamikara and P. Bertok and D. Liu and S. Camtepe and I. Khalil},
journal= {arXiv preprint arXiv:1806.06151},
year = {2018}
}