MASK: Efficient and privacy-preserving m-tree based biometric identification over cloud

Yang, Xiaopeng; Zhu, Hui; Wang, Fengwei; Zhang, Songnian; Lu, Rongxing; Li, Hui

doi:10.1007/s12083-021-01120-7

Cited by 9 publications

(4 citation statements)

References 30 publications

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“…Feature extraction techniques are applied to isolate essential patterns and features within signatures. This proposed system aims to optimize accuracy and efficiency in both verification and recognition, ultimately bolstering security throughout the authentication process [4].…”

Section: Review Of Related Literaturementioning

confidence: 99%

Dynamic Signature Verification Using Pattern Recognition

Ekwonwune,

Ekekwe,

Ubochi

et al. 2024

JSEA

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Dynamic signature is a biometric modality that recognizes an individual's anatomic and behavioural characteristics when signing their name. The rampant case of signature falsification (Identity Theft) was the key motivating factor for embarking on this study. This study was necessitated by the damages and dangers posed by signature forgery coupled with the intractable nature of the problem. The aim and objectives of this study is to design a proactive and responsive system that could compare two signature samples and detect the correct signature against the forged one. Dynamic Signature verification is an important biometric technique that aims to detect whether a given signature is genuine or forged. In this research work, Convolutional Neural Networks (CNNsor ConvNet) which is a class of deep, feed forward artificial neural networks that has successfully been applied to analysing visual imagery was used to train the model. The signature images are stored in a file directory structure which the Keras Python library can work with. Then the CNN was implemented in python using the Keras with the Ten-sorFlow backend to learn the patterns associated with the signature. The result showed that for the same CNNs-based network experimental result of average accuracy, the larger the training dataset, the higher the test accuracy. However, when the training dataset are insufficient, better results can be obtained. The paper concluded that by training datasets using CNNs network, 98% accuracy in the result was recorded, in the experimental part, the model achieved a high degree of accuracy in the classification of the biometric parameters used.

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Section: Review Of Related Literaturementioning

confidence: 99%

Dynamic Signature Verification Using Pattern Recognition

Ekwonwune,

Ekekwe,

Ubochi

et al. 2024

JSEA

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“…Building on the M-tree data structure and symmetric HE, Yang et al [ 61 ] proposed privacy-preserving biometric identification over the cloud, calling it MASK. With recognition accuracy maintained, MASK ensures the privacy of users’ recognition requests (e.g., face recognition) and service providers’ datasets, while greatly reducing cloud servers’ computing cost on biometric dataset searching.…”

Section: He-based Approaches To Biometric Securitymentioning

confidence: 99%

A Review of Homomorphic Encryption for Privacy-Preserving Biometrics

Yang

Wang

Cui

et al. 2023

Sensors

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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.

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“…All these schemes are implemented based on fully homomorphic encryption techniques. Although fully homomorphic encryption schemes have evolved significantly since the Gentry blueprint, introducing many new construction techniques that have led to improvements in efficiency, Yang et al [13] suggests that the computational complexity of fully homomorphic encryption is still too large for the average user to be applied in practice, even if a certain number of multiplication operations are sacrificed to improve the computational efficiency. In conclusion, the existing fully homomorphic encryption schemes are still unable to meet the practicality requirements due to the efficiency problem and are not promoted in outsourced computing with privacy protection.…”

Section: Related Workmentioning

confidence: 99%

Efficient Inverse Model for Cloud Computing and Information Based on the Symmetric Random Matrix

Cheng¹,

Cheng

2022

Mathematical Problems in Engineering

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With the continuous development of cloud storage technology, cloud storage has become a new way of storage for both enterprises and individuals. The cloud environment can support users to share data, but it can lead to some malicious users accessing or modifying data by illegal means, so access control is an important way to protect user data. In this paper, we propose a traceable attribute encryption scheme based on signature authentication and based on the information efficient inverse model of an asymmetric random matrix, which can trace the user who leaks information. A user attribute revocation scheme based on improved attribute encryption is also proposed, which enables data owners to reduce computational overhead. In cloud storage access control, the workload of the data owner increases exponentially when facing a high number of user attribute revocation problems, which are jointly decided by the data owner and the authorization center. An error proportional allocation (EPA) calculation method is developed to achieve an optimal estimation of system parameters in the lattice cipher scheme; experimental results show that the scheme has the advantages of shorter parameters, efficient computation, and lower storage load, proving that the scheme is resistant to IND-SAS-CPA under the standard model-based assumption of determining LWE semantic security against IND-SAS-CPA attacks; and it is used in a cloud file sharing (CFS) service framework, which can make sensitive cloud data free from the risk of privacy leakage.

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MASK: Efficient and privacy-preserving m-tree based biometric identification over cloud

Cited by 9 publications

References 30 publications

Dynamic Signature Verification Using Pattern Recognition

Dynamic Signature Verification Using Pattern Recognition

A Review of Homomorphic Encryption for Privacy-Preserving Biometrics

Efficient Inverse Model for Cloud Computing and Information Based on the Symmetric Random Matrix

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