Comprehensive and Improved Secure Biometric System Using Homomorphic Encryption

Wang

Cui

et al. 2023

Sensors

The advancement of biometric technology has facilitated wide applications of biometrics in law enforcement, border control, healthcare and financial identification and verification. Given the peculiarity of biometric features (e.g., unchangeability, permanence and uniqueness), the security of biometric data is a key area of research. Security and privacy are vital to enacting integrity, reliability and availability in biometric-related applications. Homomorphic encryption (HE) is concerned with data manipulation in the cryptographic domain, thus addressing the security and privacy issues faced by biometrics. This survey provides a comprehensive review of state-of-the-art HE research in the context of biometrics. Detailed analyses and discussions are conducted on various HE approaches to biometric security according to the categories of different biometric traits. Moreover, this review presents the perspective of integrating HE with other emerging technologies (e.g., machine/deep learning and blockchain) for biometric security. Finally, based on the latest development of HE in biometrics, challenges and future research directions are put forward.

Section: He-based Approaches To Biometric Securitymentioning

confidence: 99%

A Review of Homomorphic Encryption for Privacy-Preserving Biometrics

Wang

Cui

et al. 2023

Sensors

“…This allows the confidentiality of biometric data to be protected and matching to be performed between encrypted template data and encrypted query data without degrading recognition accuracy. As long as the decryption key is safe, the system is cryptographically secure [ 106 ].…”

Section: Potential Solutions and Opportunitiesmentioning

confidence: 99%

Biometrics for Internet-of-Things Security: A Review

Wang

Sahri

et al. 2021

Sensors

The large number of Internet-of-Things (IoT) devices that need interaction between smart devices and consumers makes security critical to an IoT environment. Biometrics offers an interesting window of opportunity to improve the usability and security of IoT and can play a significant role in securing a wide range of emerging IoT devices to address security challenges. The purpose of this review is to provide a comprehensive survey on the current biometrics research in IoT security, especially focusing on two important aspects, authentication and encryption. Regarding authentication, contemporary biometric-based authentication systems for IoT are discussed and classified based on different biometric traits and the number of biometric traits employed in the system. As for encryption, biometric-cryptographic systems, which integrate biometrics with cryptography and take advantage of both to provide enhanced security for IoT, are thoroughly reviewed and discussed. Moreover, challenges arising from applying biometrics to IoT and potential solutions are identified and analyzed. With an insight into the state-of-the-art research in biometrics for IoT security, this review paper helps advance the study in the field and assists researchers in gaining a good understanding of forward-looking issues and future research directions.

“…In these schemes, the biometric identification scheme is considered to be a two-party system, where the data owner takes charge of biometric dataset management and template matching. Most of these schemes are designed based on the secure computation protocol [18][19][20] and homomorphic encryption [9,21,22] techniques. Although the privacy-preserving is achieved in these schemes, the data owner is required to be equipped with powerful computing ability and remarkable storage capacity in these schemes, which can hardly be satisfied in most application scenarios and thus makes these schemes unpractical.…”

Section: Related Workmentioning

confidence: 99%

An Efficient and Privacy-Preserving Biometric Identification Scheme Based on the FITing-Tree

Security and Communication Networks

Zhu

Zhang

et al. 2021

Biometric identification services have been applied to almost all aspects of life. However, how to securely and efficiently identify an individual in a huge biometric dataset is still very challenging. For one thing, biometric data is very sensitive and should be kept secure during the process of biometric identification. On the other hand, searching a biometric template in a large dataset can be very time-consuming, especially when some privacy-preserving measures are adopted. To address this problem, we propose an efficient and privacy-preserving biometric identification scheme based on the FITing-tree, iDistance, and a symmetric homomorphic encryption (SHE) scheme with two cloud servers. With our proposed scheme, the privacy of the user’s identification request and service provider’s dataset is guaranteed, while the computational costs of the cloud servers in searching the biometric dataset can be kept at an acceptable level. Detailed security analysis shows that the privacy of both the biometric dataset and biometric identification request is well protected during the identification service. In addition, we implement our proposed scheme and compare it to a previously reported M-Tree based privacy-preserving identification scheme in terms of computational and communication costs. Experimental results demonstrate that our proposed scheme is indeed efficient in terms of computational and communication costs while identifying a biometric template in a large dataset.