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In recent years, biometrics has become widely adopted in access control systems, effectively resolving the challenges associated with password management in identity authentication.However, traditional biometric-based authentication methods often lead to the loss or leakage of users’ biometric data, compromising the reliability of biometric authentication.In the literature, two primary technical approaches have been proposed to address these issues.The first approach involves processing the extracted biometric data in a way that the authentication information used in the final stage or stored in the database does not contain the original biometric data.The second approach entails writing the biometric data onto a smart card and utilizing the smart card to generate the private key for public key cryptography.To address the challenge of constructing the private key of a public key cryptosystem based on fingerprint data without relying on a smart card, a detailed study was conducted on the stable feature points and stable feature distances of fingerprints.This study involved the extraction and analysis of fingerprint minutiae.Calculation methods were presented for sets of stable feature points, sets of equidistant stable feature points, sets of key feature points, and sets of truncated means.Based on the feature distance truncated mean, an original fingerprint key generation algorithm and key update strategy were proposed.This scheme enables the reconstruction of the fingerprint key through re-collecting fingerprints, without the need for direct storage of the key.The revocation and update of the fingerprint key were achieved through a salted hash function, which solved the problem of converting ambiguous fingerprint data into precise key data.Experiments prove that the probability of successfully reconstructing the fingerprint key by re-collecting fingerprints ten times is 0.7354, and the probability of reconstructing the fingerprint key by re-collecting fingerprints sixty times is 98.06%.

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Aiming at the problem of inconsistent biometric multi-modal fusion technology framework for mobile devices and lack of standards, a unified technical framework of multi-modal fusion standards was proposed.Firstly, the current status of standardization related to biometric used with mobile device was analyzed.Secondly, the local recognition and remote recognition application modes of mobile device biometrics standards were studied, and then four sub-multimodal fusion classification methods of multi-feature, multi-algorithm, multi-instance, and multi-sensor were analyzed and proposed.Then, four levels of multi-modal fusion were researched and proposed: sample-level fusion, feature-level fusion, score-level fusion, and decision-level fusion, and a standard technical framework for biometric multi-modal fusion for mobile devices was proposed.Finally, prospects for the application of biometric multi-modal fusion technology were given.

Copyright of Telecommunications Science is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In post-quantum cryptography, there are security flaws in the key exchange protocol that are easy to lose passwords and difficult to achieve mutual authentication. This paper proposed a new biometric authentication key exchange protocol based on ring learning with error. The constructed cryptosystem according to the RLWE problem had the advantages of short key and ciphertext size, and high operational efficiency. It used biometrics and passwords as long-term keys. The Peikert error coordination mechanism extracted random and uniform session keys from their respective ring elements, which realized explicit authentication of servers to customers. The performance analysis results show that the scheme can resist user fake attacks, have higher security attributes and improve communication efficiency. [ABSTRACT FROM AUTHOR]

In order to enable the Diffie-Hellman-style key exchange protocol on the lattice to achieve authentication and is suitable for large-scale communication in the client-server-client mode, a two-factor three-party authentication key exchange protocol based on Ring Learning With Error (RLWE) is proposed. The protocol uses passwords and biometrics as long-term keys for the client, enabling the server to explicitly authenticate the client. Firstly, the advantages of the difficult problem of error learning on the ring (short key and cipher text size and high operating efficiency) are used to construct the cryptosystem. Secondly，the server passes ring elements through password and biometric hash values，and combines I>type error coordination. The mechanism enables the communicating party to obtain a random and even session key. The final analysis shows that the protocol is suitable for large-scale communication, improves the communication volume，has higher security attributes, and can resist the password impersonation attacks of users. [ABSTRACT FROM AUTHOR]

In order to guarantee the information security of mobile phones, a user authentication scheme based on a mobile phone with three-axial embedded accelerometer Is proposed. It utilizes an accelerometer to capture the acceleration signal of a person, s authenticating gesture. We propose a new algorithm called Difference Bottom-Up (DBU) to achieve the automatic detection of gesture endpoints. After denoising the acceleration data streams by wavelet packet decomposition, we adopt the Dynamic Time Warping (DTW) method to calculate the similarities between test gesture and reference gesture, and the results of authentication are obtained from these similarities. Compared with the existing methods, the proposed DBU algorithm is more precise in automatic gesture endpoint detection. The authentication experimental results demonstrate that the false rejection rate Is below 5% whole the false acceptance rate is 0%, which shows the effectiveness of the scheme. [ABSTRACT FROM AUTHOR]