A Clustering Approach for the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M1"><mml:mrow><mml:mi>l</mml:mi></mml:mrow></mml:math>-Diversity Model in Privacy Preserving Data Mining Using Fractional Calculus-Bacterial Foraging Optimization Algorithm 论文

摘要

In privacy preserving data mining, the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M2"><mml:mrow><mml:mi>l</mml:mi></mml:mrow></mml:math>-diversity and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M3"><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:math>-anonymity models are the most widely used for preserving the sensitive private information of an individual. Out of these two, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M4"><mml:mrow><mml:mi>l</mml:mi></mml:mrow></mml:math>-diversity model gives better privacy and lesser information loss as compared to the <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M5"><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:math>-anonymity model. In addition, we observe that numerous clustering algorithms have been proposed in data mining, namely, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M6"><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:math>-means, PSO, ACO, and BFO. Amongst them, the BFO algorithm is more stable and faster as compared to all others except <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M7"><mml:mrow><mml:mi>k</mml:mi></mml:mrow></mml:math>-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 <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" id="M8"><mml:mrow><mml:mi>l</mml:mi></mml:mrow></mml:math>-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.