A Clustering Approach Using Fractional Calculus-Bacterial Foraging Optimization Algorithm for k-Anonymization in Privacy Preserving Data Mining

Abstract

A tremendous amount of personal data of an individual is being collected and analyzed using data mining techniques. Such collected data, however, may also contain sensitive data about an individual. Thus, when analyzing such data, individual privacy can be breached. Therefore, to preserve individual privacy, one can find numerous approaches proposed for the same in the literature. One of the solutions proposed in the literature is k-anonymity which is used along with the clustering approach. During the investigation, the authors observed that the k-anonymization based clustering approaches all the times result in the loss of information. This paper presents a fractional calculus-based bacterial foraging optimization algorithm (FC-BFO) to generate an optimal cluster. In addition to this, the authors utilize the concept of fractional calculus (FC) in the chemotaxis step of a bacterial foraging optimization (BFO) algorithm. The main objective is to improve the optimization ability of the BFO algorithm. The authors also evaluate their proposed FC-BFO algorithm, empirically, focusing on information loss and execution time as a vital metric. The experimental evaluations show that our proposed FC-BFO algorithm generates an optimal cluster with lesser information loss as compared with the existing clustering approaches.