Privacy Preserving Data Mining Operations without Disrupting Data Quality

Abstract

Data mining operations help discover business intelligence from historical data. The extracted business intelligence or actionable knowledge helps in taking well informed decisions that leads to profit to the organization that makes use of it. While performing mining privacy of data has to be given utmost importance. To achieve this PPDM (Privacy Preserving Data Mining) came into existence by sanitizing database that prevents discovery of association rules. However, this leads to modification of data and thus disrupting the quality of data. This paper proposes a new technique and algorithms that can perform privacy preserving data mining operations while ensuring that the data quality is not lost. The empirical results revealed that the proposed technique is useful and can be used in real world applications.