Performance Analysis of the Platfora Method for Privacy Preservation in Large Healthcare Datasets: An Empirical Study (Journal of Advances and Scholarly Researches in Allied Education, 2024)
Multimedia files and private medical information are among the many kinds of data stored in these systems. Data mining offers a promising method for many businesses and organizations to sift through massive data stores in search of useful insights. There are legitimate worries about privacy invasion and data abuse associated with extracting sensitive information from these databases. Personal information such as names, ages, residences, and phone numbers is included in healthcare data, along with sensitive details such the names and characteristics of diseases. Improper handling of this information might lead to its misuse for personal advantage. Hence, it is crucial to conceal data from enemies before sharing it with outside parties. One new approach that promises to solve these privacy problems is privacy-preserving data mining, or PPDM. Protecting private data from unauthorized third-party suppliers is the primary objective of PPDM. Various methods for protecting personal information have been proposed in this study. To conduct the experiment, various patient pilot datasets were considered. We apply a JAYA-based genetic algorithm to conceal information in our contribution.