Publication

Abstract : Due to the increasing adoption of cloud computing, which is adopted for its flexibility and cost-effectiveness, huge amounts of sensitive information are entrusted to cloud storage systems. However, a crucial point of contention in this growth is ensuring data security and confidentiality. Traditional cryptographic access control techniques provide a level of protection but are insufficient for guaranteeing complete trust. This paper deals with the persistent trust issues with access control methods, therefore, decreasing the security risks and increasing data owners' confidence regarding the data stored. We propose a new Task-Role based access model(T-RBAC), tailored to the cloud environment, with a holistic framework for trust evaluation, so that trust is dynamically evaluated with broad criteria for adaptation to different contexts and scenarios. Unlike most of the existing trust-based modules, the T-RBAC system combines real-time contextual factors with machine learning algorithms that predict trends of trustworthiness to aid in decision-making. The high security offered by our framework is also balanced with flexibility and scalability to allow seamless integration into existing cloud storage systems. The model will maintain long-term reliability through continuous monitoring and trust recalibration. Through extensive simulations and real-world case studies in healthcare, finance, and critical infrastructure, the computational efficiency and robustness of the T-RBAC system are measured. The T-RBAC framework, enhanced to counter the shortcomings of static RBAC models and existing modules of trust, represents an important leap forward in the security of clouds. The proposed model provides a balanced means of securing sensitive data, while enabling data owners to make better-informed decisions, thereby making way for innovative trust management in cloud computing