A Secure Model for Digital Forensics Integrating Authentication and Optimal Key Encryption

Abstract: In today's digital world, focused on cloud services, keeping forensic data secure and intact is very important. The increasing reliance on cloud and IoT systems has exposed vulnerabilities associated with centralized storage and evidence handling. This paper introduces a decentralized forensic framework called DFA-AOKGE, which incorporates robust authentication mechanisms and optimal key generation encryption to address the limitations of conventional systems. The proposed model utilizes biometric authentication integrated with blockchain for transparent audit trails and implements Enhanced Elliptic Optimization (EEO) for dynamic secret key creation. Additionally, Secure Biometric Verification Mechanisms (SBVM) and multi-key homomorphic encryption ensure end-to-end data protection, even during cloud processing. The architecture supports distributed data collection, reducing single-point failure and improving accountability. This approach overcomes challenges in digital evidence integrity, data leakage prevention, and user trust enhancement. The evaluation of the framework shows it does a good job with secure storage, privacy protection, and system scaling. This makes it a useful contribution to digital forensics.

Digital forensics, decentralized authentication, optimal key generation, homomorphic encryption, cloud security, blockchain, biometric authentication, data integrity

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