Efficient clustering technique for k-anonymisation with aid of optimal KFCM

摘要

The k-anonymity model is a simple and practical approach for data privacy preservation. To minimise the information loss due to anonymisation, it is crucial to group similar data together and then anonymises each group individually. So that in this paper proposes a novel clustering method for conducting the k-anonymity model effectively. The clustering will be done by an optimal kernel based fuzzy c-means clustering algorithm (KFCM). In KFCM, the original Euclidean distance in the FCM is replaced by a kernel-induced distance. Here the objective function of the kernel fuzzy c-means clustering algorithm is optimised with the help of modified grey wolf optimisation algorithm (MGWO). Based on that, the collected data is grouped in an effective manner. The performance of the proposed technique is evaluated by means of information loss, time taken to group the available data. The proposed technique will be implemented in the working platform of MATLAB.