Clustering-assisted privacy perseveration model for data mining

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.