Comprehensive Analysis of Privacy Preserving Data Mining Algorithms for Future Develop Trends

Abstract

The present trend of digitalization involves data sharing between various organizations such as hospitals, insurance firms, banks, stock market, and other businesses. Enormous amount of data is burgeoning at an exponential rate. Digitizing technology has entered every field, including various digital gadgets and applications used in our daily life. This data is used by analytics to aid in decision-making, understanding customer behavior, predicting market trends, etc. Despite the benefits offered by data mining and analysis, it presents some serious issues related to data privacy and security. Privacy Preserving Data Mining, PPDM, is an application of data mining that addresses these concerns. Various PPDM methods attempt to prevent sensitive data and identity disclosure by applying some transformations to the data. The main challenge is to maintain data quality for good classification accuracy while preserving data privacy. This study examines a number of privacy-related risks. In addition to this, concepts related to privacy preservation with data mining is the primary subject of this article. Understanding all of the findings presented will help one comprehend various challenges faced by PPDM techniques. Additionally, it will assist in learning and using the most appropriate strategy for any data scenario.