Efficient privacy preservation of big data for accurate data mining
This addresses the need for scalable and accurate privacy preservation in big data analysis, though it appears incremental as it builds on existing perturbation methods.
The paper tackles the problem of efficiently preserving privacy in big data for accurate data mining by proposing PABIDOT, a nonreversible perturbation algorithm using optimal geometric transformations, which showed superior execution speed, scalability, attack resistance, and accuracy compared to two other algorithms in experiments with nine datasets and five classifiers.
Computing technologies pervade physical spaces and human lives, and produce a vast amount of data that is available for analysis. However, there is a growing concern that potentially sensitive data may become public if the collected data are not appropriately sanitized before being released for investigation. Although there are more than a few privacy-preserving methods available, they are not efficient, scalable or have problems with data utility, and/or privacy. This paper addresses these issues by proposing an efficient and scalable nonreversible perturbation algorithm, PABIDOT, for privacy preservation of big data via optimal geometric transformations. PABIDOT was tested for efficiency, scalability, resistance, and accuracy using nine datasets and five classification algorithms. Experiments show that PABIDOT excels in execution speed, scalability, attack resistance and accuracy in large-scale privacy-preserving data classification when compared with two other, related privacy-preserving algorithms.