Dynamic data auditing scheme for big data storage

Abstract

When users store data in big data platforms, the integrity of outsourced data is a major concern for data owners due to the lack of direct control over the data. However, the existing remote data auditing schemes for big data platforms are only applicable to static data. In order to verify the integrity of dynamic data in a Hadoop big data platform, we presents a dynamic auditing scheme meeting the special requirement of Hadoop. Concretely, a new data structure, namely Data Block Index Table, is designed to support dynamic data operations on HDFS (Hadoop distributed file system), including appending, inserting, deleting, and modifying. Then combined with the MapReduce framework, a dynamic auditing algorithm is designed to audit the data on HDFS concurrently. Analysis shows that the proposed scheme is secure enough to resist forge attack, replace attack and replay attack on big data platform. It is also efficient in both computation and communication.