Efficient Data Perturbation for Privacy Preserving and Accurate Data Stream Mining

M. A. P. Chamikara; P. Bertok; D. Liu; S. Camtepe; I. Khalil

doi:10.1016/j.pmcj.2018.05.003

paper

Efficient Data Perturbation for Privacy Preserving and Accurate Data Stream Mining

arXiv:1806.06151 · doi:10.1016/j.pmcj.2018.05.003

Abstract

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: 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.

Pervasive and Mobile Computing 2018