Privacy-preserving association rule mining: a survey of techniques for sensitive rule identification and enhanced data protection

Abstract

Over the past few decades, pervasive computing has generated increasing amounts of data. Data mining (DM) technology is becoming more widely used. Due to the potential threats to the confidentiality of people and organizations, the massive collecting and analysis of data might raise privacy issues. A new research area termed privacy-preserving association rule mining (PPARM), which has attracted the interest of many researchers, has emerged to prevent privacy disclosure during DM. The research field PPARM, aims to prevent association rule mining privacy infractions from happening. The study uses a systematic survey to understand the literature on PPARM techniques for sensitive rule identification and enhanced data protection. Here, the application area of PPARM has been explored, summarized, and concluded with some open challenges and future directions.