Abstract

Association Rule Mining has become one of the most influential techniques in data mining allowing organizations to discover hidden patterns and meaningful correlations from large datasets. However with the growing reliance on digital databases across healthcare banking retail telecommunications and e-governance concerns over privacy leakage have increased significantly. Sensitive attributes personal identifiers and confidential business information can unintentionally be exposed through mining processes especially when traditional algorithms operate on unprotected raw data. This research focuses on privacy-preserving association rule mining (PP-ARM) a domain that attempts to balance accurate knowledge discovery with the stringent need to protect individual privacy. The study explores major privacy-preserving techniques such as anonymization randomization secure multiparty computation differential privacy cryptographic transformation and heuristic data distortion. It evaluates their abi