PPRF: Privacy Preserving Random Forest for Data Stream Mining

Abstract

In today’s technology-driven landscape, the perva- sive use of online services across diverse domains has to led the generation of vast datasets, necessitating advanced data mining techniques for meaningful insights. The advent of data streams, characterized by continuous and dynamic data flows, presents a significant challenge, prompting the  evolution  of data stream mining. This field addresses issues such as rapid changes in streaming data and the need for quick algorithms. To tackle these challenges, an innovative approach named (Effective Privacy Preserving eXtreme Gradient Boosting) PPRF is proposed, combining Adaptive XGBOOST for continuous learning from evolving data streams with PPXGBOOST for privacy preservation..