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arXiv · 1806.06151

Efficient Data Perturbation for Privacy Preserving and Accurate Data Stream Mining

M. A. P. Chamikara·P. Bertok·D. Liu·S. Camtepe·I. Khalil
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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 $P^2RoCAl$). $P^2RoCAl$ 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.

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M. A. P. Chamikara, P. Bertok, D. Liu, S. Camtepe, I. Khalil. 2018-06-15. Efficient Data Perturbation for Privacy Preserving and Accurate Data Stream Mining. https://doi.org/10.1016/j.pmcj.2018.05.003

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