An Efficient Public Data Integrity for Big Data Processing System In Multiple Cloud Storage

Abstract

Big data processing is progressively becoming essential for everyone to extract the meaningful information from their large volume of data irrespective of types of users and their application areas. Big data processing is a broad term and includes several operations such as the storage, cleaning, organization, modelling, analysis and presentation of data at a scale and efficiency. For ordinary users, the significant challenges are the requirement of the powerful data processing system and its provisioning, installation of complex big data analytics and difficulty in their usage. However, this form of storage introduces new security challenges, such as unreliable service providers. Data storage correctness is another challenge that should be addressed before this modern storage model can be extensively applied. Hence, a new scheme is introduced for securing data integrity via a multiple third party auditors based mutual authentication to overcome the aforementioned limitations and ensure high-level security. This system is an inexpensive and user-friendly framework for everyone who has the knowledge of basic IT skills. The performance of this analysis is calculated on different aspects such as Accuracy, Precision, and security. Though the outcomes of various methods are different, efficient public data integrity for big data processing system in multiple cloud storage will be the best results in this analysis.