Performance analysis of perturbation-based privacy preserving techniques: an experimental perspective

<span lang="EN-US">Nowadays, enormous amounts of data are produced every second. These also contain private information from sources including media platforms, the banking sector, finance, healthcare, and criminal histories. Data mining is a method for looking through analyzing massive volumes to find usable information. Preserving personal during has become difficult, thus privacy-preserving (PPDM) used do so. perturbation one several tactics by PPDM privacy protection mechanism. In perturbation, datasets perturbed in order preserve Both accuracy addressed it. This paper will explore compare hybrid strategies that may be protect privacy. For this, two perturbation-based techniques named improved random projection (IRPP) enhanced principal component analysis-based technique (EPCAT) were used. methods employed assess precision, run time, experimental results. provides impacts preserving techniques. It observed approaches more efficient than traditional approach.</span>