A Dynamic Adaptive Access Control Scheme for Hadoop Platform

The advent of the era of big data has made data an important economic asset. Apache Hadoop has become the main platform for processing large amounts of data with its excellent data storage capacity and distributed computing advantages. The data stored by the Hadoop platform from different enterprises, organizations or institutions often contains private and sensitive information, but the big data platform faces many types of access subjects. To ensure that this information is protected from unauthorized access, and to meet the ever-changing needs of users, it is no longer possible to rely solely on traditional access control techniques based on static rules. By analyzing the Hadoop system log, tracking user access behavior trajectory, this paper proposes a dynamic adaptive access control scheme for Hadoop platform that adopts user suspicious status evaluation and user authorization policy based on labels and attributes. This scheme can realize the real-time dynamic adjustment of user authority according to user behavior by designing the trigger mechanism of authority automatic change, thereby more effectively protecting user sensitive information and private data in a big data environment.