Blockchain-Driven Trust And Reputation Model For Big Data Processing In Multi-Cloud Environments

Abstract

Big data processing is increasingly being done on the cloud, which has sparked questions about the dependability and credibility of cloud providers. Conventional trust models are frequently centralized and prone to manipulation. This study suggests a blockchain-based trust and reputation model to enhance cloud provider selection in multi-cloud settings. For assessing cloud service trust, the model combines blockchain technology, smart contracts, federated trust evaluation, reinforcement learning, fuzzy logic, and Bayesian inference to produce an open, decentralized, and impenetrable framework. By achieving trust accuracy (92.4%), scalability (88.6%), accuracy (94.3%), and efficiency (90.8%) through experimental simulations, the suggested method outperforms conventional models, indicating its potential for more precise and scalable assessments of cloud services. The blockchain-driven method offers a thorough and flexible way to examine cloud providers while improving security by automating trust assessments and lowering the possibility of fraud. By minimizing false claims and guaranteeing that trust is appropriately computed, this approach optimizes the provider selection procedure for large data processes. The findings demonstrate that the suggested model is a strong foundation for managing trust in dynamic cloud settings and that it offers safe, transparent, and effective evaluations for multi-cloud ecosystems. Real-world deployment and additional optimization can be the focus of future research.