Big Data Platform Access Control Rule Generation Method Based on Data Mining

Abstract

The access control problem of big data platform is related to personal privacy, corporate interests and national security. In the context of big data platform, the fine-grained permission relationship between users and resources is difficult to be explored through the experience of administrators, and the permission granularity is difficult to be refined. In this paper, we propose an access control rule generation method based on data mining. By selecting appropriate data preprocessing, clustering analysis, association analysis algorithms and improving them, we dig out the normal access behavior rules of users from the user logs and attributes, and generate fine-grained control rules based on these rules, and improve the accuracy through negative feedback regulation. The experiment results verify the effectiveness and practicability of the method which can provide accurate access control for the big data platform.