Section Article

Investigation into Association Rule Mining with Privacy Preservation
Author(s): Jagjeet Khurana

Abstract
The emergence of data-driven technologies across diverse industrial commercial and governmental domains has intensified the importance of Association Rule Mining (ARM) as a foundational technique for uncovering significant patterns embedded within large datasets. ARM has become indispensable in fields such as e-commerce healthcare banking consumer analytics fraud detection intrusion monitoring and digital marketing. However with increasing concerns surrounding data misuse digital surveillance and unauthorised inference of personal information the traditional implementation of ARM has become insufficient. This research paper investigates the critical intersection of ARM and privacy preservation as data mining without appropriate safeguards can inadvertently expose sensitive attributes behavioural patterns or confidential associations about individuals or organisations. The abstract underscores that privacy-preserving data mining (PPDM) has emerged as a sophisticated research direction to ensure that ARM processes extract meaningful rules while preventing leakage of personally identifiable or confidential information.