Title: Clustering-assisted privacy perseveration model for data mining

Authors: S. Mohana; T.M. Nithya; Sardar Khan Nikkath Bushra; Ramakrishnan Vasanthi; K.S. Guruprakash; Sudha Rajesh

Addresses: Department of Computer Science and Engineering, Saranathan College of Engineering, Tiruchirappalli, Tamil Nadu 620012, India ' Department of Computer Science and Engineering, K. Ramakrishnan College of Engineering, Samayapuram, Trichirappalli 621112, India ' Department of Computing Technologies, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu District, Chennai-603203, India ' Department of Computer Science and Engineering, St. Joseph's Institute of Technology, Old Mahabalipuram Rd., Kamaraj Nagar, Semmancheri, Chennai, Tamil Nadu 600119, India ' Department of Computer Science and Engineering, K. Ramakrishnan College of Engineering, Samayapuram-Kariyamanickam Rd., Tamil Nadu 621112, India ' Department of Computational Intelligence, SRM Institute of Science and Technology, Chennai, Kattankulathur, Chengalpattu, Tamil Nadu, India

Abstract: Data mining techniques are used to examine the data in order to reveal hidden patterns. While preserving the privacy of individual records, privacy preserving data mining (PPDM) technology enables us to extract meaningful information from massive volumes of data. This paper proposes the two stages of the privacy preservation are data sanitisation and data restoration. The clustering, key generation and key pruning elements of the data sanitisation process are all carried out in a distributed environment. The key is pruned using the deep maxout model to make any last modifications after being formed using the hybrid optimisation, Tasmanian updated Namib beetle optimisation (TUNBO), which combines the Tasmanian devil optimisation (TDO) and Namib beetle optimisation (NBO) algorithms. In the data restoration step, which is the reverse of sanitisation, the sanitised data is also retrieved. In the meantime, the correlation coefficients are 85.64%, 88.76%, 75.94%, 74.67%, and 82.67%, compared to other models.

Keywords: data mining; privacy preservation; deep maxout; k-means; hybrid optimisation.

DOI: 10.1504/IJAHUC.2024.141961

International Journal of Ad Hoc and Ubiquitous Computing, 2024 Vol.47 No.2, pp.108 - 125

Received: 08 May 2023
Accepted: 25 Mar 2024
Published online: 03 Oct 2024 *

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