Dynamic Data Auditing Using MongoDB in Cloud Platform

Abstract

Shared computing resources in a pool enable various services that accessed over Internet for storing large company data. It has reduced the cost and infrastructural needs but there is a concern for data safety and integrity. The existing remote data auditing technique integrates the application of algebraic signature properties of a cloud and new architecture, and divides rule table to allow users to carry out data manipulations quickly. However, majority of the auditing techniques are done on static dataset and also incurs a computational overhead when data size increases. In this paper, dynamic data updating which is a pivotal function in data auditing is used. To reduce the overhead and increase the computational efficiency, the proposed system uses MongoDB. This enables the system to be scaled to larger files and also reduces the computation time elapsed in identifying updated data by using JSON format of stored files in the database. Additionally, the security concern and client’s overhead are minimized by using RSA signatures to conserve the confidentiality of the data uploaded by the cloud consumer. The auditor quickly and efficiently scans only the updated data block for any viruses or malware, thereby reducing the cost and computational power requirements of the third-party auditor and also improves the overall speed and efficiency, encouraging more people to approach the cloud space with trust.