Title: Privacy preserving data mining using hiding maximum utility item first algorithm by means of grey wolf optimisation algorithm

Authors: M.T. Ketthari; Sugumar Rajendran

Addresses: St. Peter's University, Avadi, Chennai, India ' Department of Computer Science and Engineering, Velammal Institute of Technology, Chennai, India

Abstract: In the privacy preserving data mining, the utility mining casts a very vital part. The objective of the suggested technique is performed by concealing the high sensitive item sets with the help of the hiding maximum utility item first (HMUIF) algorithm, which effectively evaluates the sensitive item sets by effectively exploiting the user defined utility threshold value. It successfully attempts to estimate the sensitive item sets by utilising optimal threshold value, by means of the grey wolf optimisation (GWO) algorithm. The optimised threshold value is then checked for its performance analysis by employing several constraints like the HF, MC and DIS. The novel technique is performed and the optimal threshold resultant item sets are assessed and contrasted with those of diverse optimisation approaches. The novel HMUIF considerably cuts down the calculation complication, thereby paving the way for the enhancement in hiding performance of the item sets.

Keywords: data mining; privacy preserving utility mining; sensitive item sets; optimal threshold; grey wolf optimisation; GWO.

DOI: 10.1504/IJBIDM.2019.098839

International Journal of Business Intelligence and Data Mining, 2019 Vol.14 No.3, pp.401 - 418

Received: 23 Nov 2016
Accepted: 25 Feb 2017
Published online: 04 Apr 2019 *

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