IoT - Enabled Secure and Scalable Cloud Architecture for Multi - User Systems: A Hybrid Post - Quantum Cryptographic and Block Chain based Approach Towards a Trustworthy Cloud Computing

Cloud computing remains one of the most significant advancements in resource provision to organizations in the latest decades since they initiate conveniences on demand access. The adoption of the IoT has brought about a new era of collaborative computing by employing a pool of linked smart sensors and devices that will produce and analyse great amounts of data, thus creating new related problems in terms of size and security, hence increasing even more the importance of traditional security measures. Therefore, in this paper, we synthesize a new SSCA that combines IoT and cryptographic mechanisms, to build highly available and secure cloud systems, which makes these systems multi - users and fully usable by many users at the same time. The design resolves to a distributed structure where sever al cloud nodes will address user s requests proficiently and it uses Multicast and Broadcast Rekeying Algorithm (MBRA) to maintain the privacy and the confidentiality of the users? detail, employing a cryptosystem that integrates MBRA, Post Quantum Cryptography (PQC) and block chain. The architecture using IoT devices collects data from the distributed sensing resources through the data storage layer; it enforces the security integrity of the information collected by employing the MBRA - PQC encryption algorithms; the blockchain provides the security for the confidential data by storing it in unalterable and dispersed registers. The proposed approach is then demonstrated with different data sets and its performance is measured based on the response time, the throughput and scaleability, security and reliability. The outcome of the expressions reveals the efficiency of the proposed SSCA compared to MHE - IS - CPMT as it reduced by 1.67 seconds and 0.97 seconds at 250 and 1000 devices respectively. Similarly, the result as AUC value for SSCA showed a better improvement than the MHE - IS - CPMT, EAM, SCSS, and SHCEF models with improvements in percent at 25 - user level: 6.30%, 6.90%, 7.60%, and 7.30%, respectively as well as; at 50 - user level: 5.20%, 9.30%, 11.50%.