A Clustering Approach for the -Diversity Model in Privacy Preserving Data Mining Using Fractional Calculus-Bacterial Foraging Optimization Algorithm

In privacy preserving data mining, the -diversity and -anonymity models are the most widely used for preserving the sensitive private information of an individual. Out of these two, -diversity model gives better privacy and lesser information loss as compared to the -anonymity model. In addition, we observe that numerous clustering algorithms have been proposed in data mining, namely, -means, PSO, ACO, and BFO. Amongst them, the BFO algorithm is more stable and faster as compared to all others except -means. However, BFO algorithm suffers from poor convergence behavior as compared to other optimization algorithms. We also observed that the current literature lacks any approaches that apply BFO with -diversity model to realize privacy preservation in data mining. Motivated by this observation, we propose here an approach that uses fractional calculus (FC) in the chemotaxis step of the BFO algorithm. The FC is used to boost the computational performance of the algorithm. We also evaluate our proposed FC-BFO and BFO algorithms empirically, focusing on information loss and execution time as vital metrics. The experimental evaluation shows that our proposed FC-BFO algorithm derives an optimal cluster as compared to the original BFO algorithm and existing clustering algorithms. 1. Introduction An immense amount of personal data of an individual is collected by various organizations, namely, e-banking, online shopping, and medical and insurance agencies. Such collected data of an individual can be further analyzed digitally to find out useful information for various purposes such as medical research and market trend analysis. On one hand, data mining techniques are being used to find out the useful information from the collected data. As compared, the collected data might contain sensitive personal information. Therefore, mining such collected data can potentially disclose individuals’ personal sensitive information. As a consequence, the personal sensitive information needs to be protected before conducting the data mining. For this reason, preserving the privacy of an individual becomes a prime research issue in the privacy preserving data mining [1]. To achieve the objective of privacy in the privacy preserving data mining, two main approaches have been proposed in the literature, namely, cryptographic approaches [2, 3] and the anonymization based approaches [4–18]. However, our focus here is on anonymization approaches only, owing to the lesser communication and computation cost of the same as compared to their cryptographic counterpart [2, 3]. Among the