Efficient Privacy-Preserving Association Rule Mining with Map-Reduce

Knowledge discovery from databases is a common goal of data mining and has attracted much attention from researchers. However, data often contains sensitive information, and privacy-preserving association rule mining (PARM) to avoid sensitive information disclosure is an urgent research direction. This paper presents the application of the Map-Reduce model to an improved privacy-preserving association rule mining algorithm. This is an approach to create efficient and time-saving tools. Experimental results show that the proposed model's running time is improved by minimizing sensitive association rules.