A Survey on Biometric Authentication Systems in Cloud to Combat Identity Theft

doi:10.31838/jcr.07.03.97

Cited by 12 publications

(5 citation statements)

References 14 publications

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“…According to this survey, it is crucial to increase the security and privacy of biometric authentication systems. In order to prevent theft of identities, Paper [13] presented a state-of-the-art survey of the various authentication techniques available in cloud environments. An effective authentication system that ensures security against data theft has been the focus of numerous research studies.…”

Section: Related Workmentioning

confidence: 99%

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SSO Based Fingerprint Authentication of Cloud Services for Organizations

Ali Zaheer Agha

2023

IJRITCC

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Access to a pool of programmable resources, such as storage space, applications, services, and on-demand networks, is made possible by cloud computing technology. Involving the cloud with the organization reduces its efforts to meet the needs of its customers. The Single Sign-On (SSO) method, which enables users to access various application services using a single user credential, is one of the key benefits of cloud computing. There are numerous problems and difficulties with cloud computing that need to be highlighted. However, protecting user agent privacy against security assaults is far more challenging. To combat security and privacy assaults, this study suggests SSO-based biometric authentication architecture for cloud computing services. Since end devices are computationally inefficient for processing user information during authentication, biometric authentication is effective for resources controlled by end devices at the time of accessing cloud services. As a result, the proposed design minimizes security attacks in cloud computing. An innovative strategy that establishes a one-to-one interaction between the user agent and the service provider is also included in the suggested design. In this case, user agents can use their fingerprint to access various cloud application services and seek registration. The highlights of the suggested architecture have been offered based on comparison analysis with a number of existing architectures.

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Section: Related Workmentioning

confidence: 99%

“…Robustness of the authorized templates More complex to analyze the system [13] PIN and random salting are used to create the cancelable biometric Fingerprint templates…”

Section: Hybrid Transformati Onmentioning

confidence: 99%

SSO Based Fingerprint Authentication of Cloud Services for Organizations

Ali Zaheer Agha

2023

IJRITCC

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“…When compared to standard authentication techniques done with keys and identification cards, BA is evaluated to be more genuine and appropriate (Yadav et al, 2020). Since modalities, namely faces, fingerprints, palm vein irises, hand geometry, and voices are truly unique, the usage of biometrics is increasing each day, which makes them an effectual blockade to cyber criminals trying to underhandedly impersonate us (Carmel & Akila, 2020). This system recommended several privacy-preserving biometric verification solutions.…”

Section: Introductionmentioning

confidence: 99%

Facial and Palm-Based Biometric Authentication and Data Security

Tiwari,

Jha

2024

Preprint

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Biometric Authentication (BA) is frequently used for authentication owing to its high recognition rate. The existing biometric hiding algorithms execute data embedding on areas that do not encompass key features of the biometric. Moreover, these techniques lacked authorization. Thus, a Secure Data Transfer model with BA and Blockchain (BC)-based authorization is proposed. Primarily, the data owner registers their details and the registered face and palm image undergoes pre-processing. By employing Pruned Residual Network 50 (PRESNET 50), the facial landmarks are extracted from the pre-processed face image. Next, Digit Folding based Log Facial Jaw Points Curve Cryptographic (DF-LFJPCC) is executed based on the jaw points to generate a secret key. Then, the Tan Sigmoid-based Convolutional Neural Network (TS-CNN) classifier is trained with the features of the pre-processed images and facial landmarks. After registration, the user logins, and their processed face and palm features are given to the trained TS-CNN for authenticating the user. The secret is also used to improve the authentication process. After successful login, the file to be uploaded is converted into cipher, which is then encrypted using Log Facial Jaw Points Curve Cryptographic (LFJPCC) and uploaded to the cloud server. In the end, authorization is performed in the BC based on the hashcode generated using Faro shuffle -Tiger (FS-Tiger) when a user requests data. As per the experimental analysis, the proposed technique outperforms prevailing models.

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Section: Introductionmentioning

confidence: 99%

“…Access control systems may also include authorization policies that specify which users are allowed to access specific resources, and what actions they are allowed to perform. Traditional access control systems are an important component of security infrastructure in many organizations, and are used to protect against theft, vandalism, data breaches, and other security threats [17,55].However, traditional access control systems have limitations, such as their inability to adapt to changing user behavior and evolving security threats. Applying machine learning to access control systems presents a promising future research direction that could enhance the accuracy, efficiency, and effectiveness of these systems.…”

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confidence: 99%

Opportunities and Challenges in Applying Machine Learning for Access Control

Nakamura¹

2023

Preprint

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Access control systems are important in regulating who can access physical facilities, computer systems, and data resources. However, traditional access control systems have limitations in adapting to changing user behavior and evolving security threats. We argue that applying machine learning is a promising direction to access control systems that can enhance their accuracy, efficiency, and effectiveness. Machine learning can help identify and mitigate security risks in real time by detecting patterns of suspicious activity that may indicate a security breach or attempted attack. It can also learn to identify anomalies and raise alerts, enabling security personnel to respond quickly and prevent potential security incidents.

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A Survey on Biometric Authentication Systems in Cloud to Combat Identity Theft

Cited by 12 publications

References 14 publications

SSO Based Fingerprint Authentication of Cloud Services for Organizations

SSO Based Fingerprint Authentication of Cloud Services for Organizations

Facial and Palm-Based Biometric Authentication and Data Security

Opportunities and Challenges in Applying Machine Learning for Access Control

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