A method to enhance privacy preservation in cloud storage through a three-layer scheme for computational intelligence...

Area of Science:

• Computer Science
• Information Security
• Distributed Systems

Background:

• Cloud computing advancements raise data control and privacy concerns due to traditional encryption vulnerabilities.
• Internal threats from cloud servers pose risks to sensitive data.
• Existing solutions often struggle to balance cloud benefits with robust data privacy.

Purpose of the Study:

• To propose a novel three-tier storage model integrating fog computing to enhance data privacy and control in cloud environments.
• To address the limitations of traditional encryption methods against internal cloud attacks.
• To optimize data distribution for improved security and storage efficiency.

Main Methods:

• Implementation of a three-tier storage architecture involving local machines, fog servers, and cloud storage.
• Utilization of the Hash-Solomon code algorithm for data partitioning and distribution.
• Application of computational intelligence to determine optimal data placement across tiers.

Main Results:

• Experimental analysis confirmed significant improvements in storage efficiency, ranging from 30% to 40% with increasing data block volumes.
• The proposed model effectively enhances data privacy by distributing data segments across multiple locations.
• Optimized data distribution across cloud, fog, and local servers ensures balanced and secure storage.

Conclusions:

• The novel framework effectively mitigates privacy concerns associated with cloud computing.
• The Hash-Solomon code method, combined with fog computing, offers a robust solution for secure and efficient data management.
• This approach successfully balances the advantages of cloud storage with enhanced data control and privacy.