Enhancing Digital Forensic Security through a Secure Storage Framework Incorporating Authentication and Optimized Key Generation Encryption

The development of secure storage models for digital forensic environments represents a significant step forward in safeguarding the integrity and confidentiality of digital evidence. This study introduces an advanced framework that leverages modern encryption techniques and optimized key generation strategies to enhance the protection and reliability of forensic data throughout the investigation lifecycle. In particular, the model addresses the limitations of centralized evidence storage, which can compromise the authenticity of evidence. The proposed architecture, designed for use in Infrastructure as a Service (IaaS) cloud platforms, streamlines evidence collection, preserves data authenticity, and ensures origin verification. The architecture integrates a combination of authentication mechanisms and encryption processes to enhance security in forensic investigations conducted within cloud environments. A key contribution of this work is the introduction of the Digital Forensic Architecture with Authentication and Optimal Key Generation Encryption (DFA-AOKGE) framework. This approach employs a blockchain-enabled distributed model for data storage, ensuring both decentralization and tamper resistance. Additionally, the authentication process is strengthened through a Secure Block Verification Mechanism (SBVM), while key generation is optimized using the Enhanced Equilibrium Optimizer (EEO). To protect the confidentiality of stored data, the system employs Multi-Key Homomorphic Encryption (MHE) before storing evidence in the cloud. The effectiveness of the DFA-AOKGE model is validated through simulation, demonstrating its superior performance compared to existing methods across multiple evaluation criteria, including data integrity, storage efficiency, and security