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

Abstract

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 Hash-Solomon 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.

Introduction

Since the early 21st century, cloud computing—especially cloud storage—has rapidly grown due to the exponential increase in user data that exceeds local storage capacities. Cloud storage enables distributed data management using networked devices but raises significant privacy and security concerns because data stored on cloud servers can be accessed by service providers or attackers. Traditional encryption protects data but cannot fully prevent internal threats or provide flexible data sharing.

To address these issues, a new approach based on a three-layer architecture—local device, fog computing nodes, and cloud servers—is proposed. This system uses a Hash-Solomon coding technique, splitting data into parts stored separately, ensuring that no single location can reconstruct the data, enhancing privacy. Fog computing extends cloud architecture by processing and storing data closer to users, improving security and efficiency.

The system integrates computational intelligence (machine learning, fuzzy logic, evolutionary algorithms) to dynamically adapt security, detect abnormal access, and improve privacy and storage management. While current cloud systems rely heavily on encryption and centralized storage, they lack flexible, adaptive, multi-layer privacy and security.

Advantages of the proposed system include stronger privacy, intelligent access control, optimized storage efficiency, and adaptive defenses against evolving cyber threats. Challenges include increased system complexity, higher computational costs, latency issues, and upfront deployment expenses. Overall, the proposed multi-layer fog-cloud architecture aims to balance data usefulness, privacy, and security in cloud storage.

Conclusion

The development of cloud computing brings us a lot of benefits. Cloud storage is a convenient technology which helps users to expand their storage capacity. However, cloud storage also causes a series of secure problems. When using cloud storage, users do not actually control the physical storage of their data and it results in the separation of ownership and management of data. In order to solve the problem of privacy protection in cloud storage, we propose a TLS framework based on fog computing model and design a Hash-Solomon algorithm. Through the theoretical safety analysis, the scheme is proved to be feasible. By allocating the ratio of data blocks stored in different servers reasonably, we can ensure the privacy of data in each server. On another hand, cracking the encoding matrix is impossible theoretically. Besides, using hash transformation can protect the fragmentary information. Through the experiment test, this scheme can efficiently complete encoding and decoding without influence of the cloud storage efficiency. Furthermore, we design a reasonable comprehensive efficiency index, in order to achieve the maximum efficiency, and we also find that the Cauchy matrix is more efficient in coding process.

References

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Copyright

Copyright © 2025 Mr. Md Saquib Ahmed S , Mrs. Jennifer Mary S. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.