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

Yang, Xiaopeng; Zhu, Hui; Zhang, Songnian; Lu, Rongxing; Gao, Xuesong

doi:10.1155/2021/2313389

Cited by 2 publications

(2 citation statements)

References 21 publications

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“…In addition, the cancelation and renewal capacity depends on the RNG's power and the algorithm's complexity to generate the encryption keys within the set of allowed elements. Some authentication systems with protection based on homomorphic encryption that calculates the squared Euclidean distance in the encrypted domain were proposed for biometric traits such as fingerprint [102], iris [103], and face [104], [105]. In addition, [106] proposed an authentication system for speaker recognition that implemented cosine similarity in the encrypted domain.…”

Section: ) Homomorphic Encryptionmentioning

confidence: 99%

A Review on Protection and Cancelable Techniques in Biometric Systems

et al. 2023

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An essential part of cloud computing, IoT, and in general the broad field of digital systems, is constituted by the mechanisms which provide access to a number of services or applications. Biometric techniques aim to manage the access to such systems based on personal data; however, some biometric traits are openly exposed in the daily life, and in consequence, they are not secret, e.g., voice or face in social networks. In many cases, biometric data are non-cancelable and non-renewable when compromised. This document examines the vulnerabilities and proposes hardware and software countermeasures for the protection and confidentiality of biometric information using randomly created supplementary information. Consequently, a taxonomy is proposed according to the operating principle and the type of supplementary information supported by protection techniques, analyzing the security, privacy, revocability, renewability, computational complexity, and distribution of biometric information. The proposed taxonomy has five categories: 1) biometric cryptosystems; 2) cancelable biometrics; 3) protection schemes based on machine learning or deep learning; 4) hybrid protection schemes; and 5) multibiometric protection schemes. Furthermore, this document proposes quantitative evaluation measures to compare the performance of protection techniques. Likewise, this research highlights the advantages of injective and linear mapping for the protection of authentication and identification systems, allowing the non-retraining of these systems when the protected biometric information is canceled and renewed. Finally, this work mentions commercial products for cancelable biometric systems and proposes future directions for adaptive and cancelable biometric systems in low-cost IoT devices.

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Section: ) Homomorphic Encryptionmentioning

confidence: 99%

A Review on Protection and Cancelable Techniques in Biometric Systems

et al. 2023

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“…The system showed a faster response time when compared to a non-clustered system, indicating that parallel Iris Recognition Approach for Preserving Privacy in Cloud Computing processing in a cloud-based environment is crucial for efficient template matching. C. Hahn et al [14] stated that the primary cause of security vulnerabilities is inadequate randomness in the encryption of biometric databases. This weakness can manifest in the form of ciphertext-only attacks and known plaintext attacks, where the attacker can observe, access, or collude with cloud providers and users to obtain sensitive information.…”

Section: Recognition Of Individuals Using Encrypted Biometric Featuresmentioning

confidence: 99%

Iris Recognition Approach for Preserving Privacy in Cloud Computing

M K,

Samanta

et al. 2023

Int. J. Onl. Eng.

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Biometric identification systems involve securing biometric traits by encrypting them using an encryption algorithm and storing them in the cloud. In recent decades, iris recognition schemes have been considered one of the most effective biometric models for identifying humans based on iris texture, due to their relevance and distinctiveness. The proposed system focuses on encrypting biometric traits. The user’s iris feature vector is encrypted and stored in the cloud. During the matching process, the user’s iris feature vector is compared with the one stored in the cloud. If it meets the threshold conditions, the user is authenticated. Iris identification in cloud computing involves several steps. First, the iris image is pre-processed to remove noise using the Hough transform. Then, the pixel values are normalized, Gabor filters are applied to extract iris features. The features are then encrypted using the AES 128-bit algorithm. Finally, the features of the test image are matched with the stored features on the cloud to verify authenticity. The process ensures the privacy and security of the iris data in cloud storage by utilizing encryption and efficient image processing techniques. The matching is performed by setting an appropriate threshold for comparison. Overall, the approach offers a significant level of safety, effectiveness, and accuracy.

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

Cited by 2 publications

References 21 publications

A Review on Protection and Cancelable Techniques in Biometric Systems

A Review on Protection and Cancelable Techniques in Biometric Systems

Iris Recognition Approach for Preserving Privacy in Cloud Computing

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