A hybrid machine learning model with self-improved optimization algorithm for trust and privacy preservation in cloud environment
Himani Saini, Gopal Singh, Sandeep Dalal, Iyyappan Moorthi, Sultan Mesfer Aldossary, Nasratullah Nuristani, Arshad Hashmi
Abstract
The rapid adoption of cloud-based data sharing is transforming collaboration across various sectors, yet ensuring trust and privacy in sensitive data remains a critical challenge. This paper presents a hybrid model aimed at enhancing data privacy and trust in cloud environments, specifically addressing concerns in healthcare and finance. The model combines k-anonymity for user privacy, an optimized Firefly algorithm for trust generation, and a Time-aware Modified Best Fit Decreasing (T-MBFD) algorithm to improve resource allocation efficiency. Key contributions include a comprehensive methodology that encompasses dataset selection, preprocessing, model training, and evaluation across multiple datasets, including healthcare, financial, and pandemic-related data. Experimental results demonstrate that the hybrid model achieves a precision score of approximately 90% and an accuracy of around 93% in financial datasets, significantly outperforming existing methods in both privacy preservation and computational efficiency. These findings emphasize the model’s effectiveness in securely facilitating data-driven collaboration in highly regulated domains, thus paving the way for practical applications that uphold individual privacy and data integrity in cloud-based environments.