Anti-spoofing cloud-based multi-spectral biometric identification system for enterprise security and privacy-preservation

Nowadays, cloud computing has provided enterprises and users with several capabilities to process and store their data in various cloud data centers. Storing and processing these sensitive data with better protection and management are a big challenge. Therefore, there is a need for maintaining the confidentiality and integrity of data in the cloud without any information leakage. Recently, biometric recognition has achieved significant advancements in the identification of individuals for the purpose of privacy-preservation in the cloud computing. Only few works have used a face as a typical biometric trait for cloud and cross-enterprise identification in the last recent years. However, current cloud-based biometric identification systems and approaches have some limitations such as noisy data, inter and intra class variations, high time cost, inaccurate, non-universality and spoofing attack. This paper proposes a new anti-spoof multispectral biometric cloud-based identification approach for privacy and security of cloud computing. The approach offers the solution using multi-spectral palmprint as a typical biometric trait between two main phases: offline enrollment phase and online identification phase. This work is considered the first approach of privacy-preservation in cloud computing using encrypted multi-spectral palmprint features without any information leakage and disclosure possibility. The experimental results show that the proposed approach can accurately and efficiently provide the privacy and security of cloud data.