Secure and privacy improved cloud user authentication in biometric multimodal multi fusion using blockchain-based lightweight deep instance-based DetectNet

ABSTRACT

This research introduces an innovative solution addressing the challenge of user authentication in cloud-based systems, emphasizing heightened security and privacy. The proposed system integrates multimodal biometrics, deep learning (Instance-based learning-based DetectNet–(IL-DN), privacy-preserving techniques, and blockchain technology. Motivated by the escalating need for robust authentication methods in the face of evolving cyber threats, the research aims to overcome the struggle between accuracy and user privacy inherent in current authentication methods. The proposed system swiftly and accurately identifies users using multimodal biometric data through IL-DN. To address privacy concerns, advanced techniques are employed to encode biometric data, ensuring user privacy. Additionally, the system utilizes blockchain technology to establish a decentralized, tamper-proof, and transparent authentication system. This is reinforced by smart contracts and an enhanced Proof of Work (PoW) mechanism. The research rigorously evaluates performance metrics, encompassing authentication accuracy, privacy preservation, security, and resource utilization, offering a comprehensive solution for secure and privacy-enhanced user authentication in cloud-based environments. This work significantly contributes to filling the existing research gap in this critical domain.

KEYWORDS:

• User authentication
• cloud security
• instance-based learning-based DetectNet
• smart contracts
• proof of work

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Data sharing is not applicable to this article as no datasets were generated or analysed during the current study.

Authorship contributions

All authors are contributed equally to this work.

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.