Computational Intelligence Enabled Three-Layer Privacy Preserving Cloud Storage for Fog Computing

Over the past few years, cloud computing has witnessed remarkable growth and evolution. The rapid expansion of formless data is driving greater research and attention to cloud storage technology. But according to the present storage structure, within existing storage models, user data is fully concentrated in cloud servers, which results in users gradually losing direct authority over their information and facing higher risks of privacy breaches. Conventional privacy safeguards primarily rely on encryption, yet such methods fall short when it comes to mitigating insider threats within cloud service environments. We suggest a fog computing- To address this concern, a layered storage framework leveraging fog computing is proposed. The suggested architecture can safeguard data privacy while utilizing cloud storage to its fullest potential. Additionally, The HashSolomon coding mechanism is employed to break data into multiple independent fragments. To preserve anonymity, we can then save a small subset of the data is stored across the user’s device and nearby fog servers. Further-more, through computational intelligence, the system dynamically determines how data should be allocated among the local device, fog nodes, and cloud servers. The viability of our plan, which is a very potent addition to the current cloud storage scheme, has been confirmed by the both security-focused theoretical studies and practical experiments validate the effectiveness of this approach.