An Architecture for Security and Protection of Big Data

Abstract

The issue of online privacy and security is a challenging subject, as it concerns the privacy of data that are increasingly more accessible via the internet. In other words, people who intend to access the private information of other users can do so more efficiently over the internet. This study is an attempt to address the privacy issue of distributed big data in the context of cloud computing. One of the cases where data privacy is of great importance is the authentication and protection of ownership data. In this paper, this privacy issue is analyzed by Petri net modeling. What today's organizations need for their clouds are integrated comprehensive solutions that can deliver security intelligence. Advanced security intelligence solutions can close security gaps by using labor-saving automation to analyze millions of events occurring within the cloud, and discover system vulnerabilities through the normalization and correlation of these events. Using the proposed method, a model of security, including control of user access to databases of big data with RMS, the multiplicity and the virtual machine to prevent internal threats, deleting data, insecure or incomplete data protection and control of a third-party can be provided to improve the operation according to the rules of Petri net modeling and simulation.