A Data Perturbation Method by Field Rotation and Binning by Averages Strategy for Privacy Preservation

Abstract

In this paper a novel technique useful to guarantee privacy of sensitive data with specific focus on numeric databases is presented. It is noticed that analysts and decision makers are interested in summary values of the data rather than the actual values. The proposed method considers that the maximum information lies in association of attributes rather than their actual proper values. Therefore it is aimed to perturb attribute associations in a controlled way, by shifting the data values of specific columns by rotating fields. The number of rotations is determined via using a support function for association rule handling and an algorithm that computes the best-choice rotation dynamically. Final summary statistics such as average, standard deviation of the numeric data are preserved by making bin average replacements for the actual values. The methods are tested on selected datasets and results are reported.