EFFECTIVE STRATEGY TO ENHANCE INTERMEDIATE DATA CONFIDENTIALITY SCIENTIFIC WORKFLOWS ON DISTRIBUTED MULTI-TENANT CLOUD INFRASTRUCTURE.

Specific data transitions and evaluations are part of a scientific workflow, these are a standard process for integrating them, based on the data correlations in the corresponding repositories. To complete a scientific computing task, a massive amount of transitional (intermediate) data and sub-processes must be constructed, each one of which may be developed by separate Virtual Machines (VMs) in the cloud. Complex scientific computations can be automated by using scientific workflows. Confidentiality demands that once an intruder amasses the intermediate data, the secrecy content cannot be accessed. Thus, this research proposes a novel method for storing and preserving the Intermediate Data (ID) relied on multi-tenant architecture that ensures data confidentiality and overall operational effectiveness. The core work is focused on ensuring confidentiality through the incorporation of Block-chain techniques. The suggested work features mechanisms for data secrecy, authentication (verification), data preservation, and information sharing in managing confidential data. We use smart contracts and the principles of Block-chain to provide safe data distribution and storage, ensuring the prevention of data breaches during sharing processes without the owner's consent. A Decentralized Confidentiality Scheme (DCS) is proposed to secure intermediate data on distributed Multi-tenant Cloud Infra. In DCS, the smart consensus is duplicated across several nodes in the Block-chain network, providing integrity, confidentiality, and longevity. Infection Monkey simulation platform is used for the experimentations. Simulated findings show that the suggested procedure is helpful in enhancing intermediate data confidentiality in scientific workflows and it ensured above 98.13% protection against potential breach. [ABSTRACT FROM AUTHOR]