Efficient and privacy-preserving biometric identification in cloud

With the rapid growth in the development of smart devices equipped with biometric sensors, client identification system using biometric traits are widely adopted across various applications. Among many biometric traits, fingerprint-based identification systems have been extensively studied and deployed. However, to adopt biometric identification systems in practical applications, two main obstacles in terms of efficiency and client privacy must be resolved simultaneously. That is, identification should be performed at an acceptable time, and only a client should have access to his/her biometric traits, which are not revocable if leaked. Until now, multiple studies have demonstrated successful protection of client biometric data; however, such systems lack efficiency that leads to excessive time utilization for identification. The most recently researched scheme shows efficiency improvements but reveals client biometric traits to other entities such as biometric database server. This violates client privacy. In this paper, we propose an efficient and privacy-preserving fingerprint identification scheme by using cloud systems. The proposed scheme extensively exploits the computation power of a cloud so that most of the laborious computations are performed by the cloud service provider. According to our experimental results on an Amazon EC2 cloud, the proposed scheme is faster than the existing schemes and guarantees client privacy by exploiting symmetric homomorphic encryption. Our security analysis shows that during identification, the client fingerprint data is not disclosed to the cloud service provider or fingerprint database server