Elevating Cloud Security With Advanced Trust Evaluation and Optimization of Hybrid Fireberg Technique

The rapid expansion of the cloud service industry has raised the critical challenge of ensuring efficient job allocation and trust within a backdrop of heightened privacy concerns. Existing models often struggle to achieve an optimal balance between these factors, particularly in dynamic cloud environments. This research introduces a comprehensive approach that optimizes trust-based job allocation in cloud services while addressing privacy issues. Our proposed hybrid model integrates k-anonymity techniques for privacy preservation, coupled with a firefly-Levenberg (Fireberg) optimization to bolster trust generation. It also employs the time-aware modified best fit decreasing (T-MBFD) allocation policy to make resource allocation time-sensitive. This strategic allocation approach enhances cloud computing system performance and scalability. Simulations using a dataset of 95,000 records demonstrate that our model achieves an impressive 96% accuracy, surpassing existing literature by 5%–14%. The results highlight the model’s ability to provide robust privacy protection while ensuring efficient resource allocation. The proposed hybrid model promises cloud service users high-quality, secure, and efficient job allocations, thereby improving customer satisfaction and trust. This research makes significant contributions to fortifying the reliability and appeal of cloud services in an evolving digital landscape.