PrivBioMTAuth: Privacy Preserving Biometrics-Based and User Centric Protocol for User Authentication From Mobile Phones

Gunasinghe, Hasini; Bertino, Elisa

doi:10.1109/tifs.2017.2777787

Cited by 64 publications

(32 citation statements)

References 25 publications

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“…Hu et al [18] improved the privacy preservation scheme to examine the processing capacity and bandwidth consumption. Gunasinghe et al [17] proposed a privacy-preserving biometric-based authentication for smart devices. Liu et al [30] aimed to design a cooperative privacy preservation that considers space-ware and timeaware contexts.…”

Section: Related Workmentioning

confidence: 99%

Chaotic-map based authenticated security framework with privacy preservation for remote point-of-care

Deebak

Al‐Turjman²,

Nayyar

2020

Multimed Tools Appl

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Section: Related Workmentioning

confidence: 99%

Chaotic-map based authenticated security framework with privacy preservation for remote point-of-care

Deebak

Al‐Turjman²,

Nayyar

2020

Multimed Tools Appl

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“…The SVM is a popular and powerful binary classifier, which aims to find a hyperplane within the feature space that separates between two classes. SVM is used by seven authentication schemes for IoT devices in edge environments using bio-features [25], [31], [32], [33], [34], [35], [36].…”

Section: A Support-vector Machine (Svm)mentioning

confidence: 99%

“…In another study, the SVM classifier was attempted as the learning algorithm by Gunasinghe and Bertino [34], face as the bio-feature , and eigen faces as the feature extraction algorithm. The trained SVM classifier helps to the artifacts stored in the Mobile IoT devices.…”

Section: A Support-vector Machine (Svm)mentioning

confidence: 99%

“…The trained SVM classifier helps to the artifacts stored in the Mobile IoT devices. Compared to Mahbub et al's [33] approach, the protocol [34] considers privacy preserving of the training data, which is uses three secrets (S i : i ∈ {1, 2, 3}) in different phases of the scheme, including, S 1 of size 128 bits, S 2 of size 160 bits, and S 3 of size 256 bits.…”

Section: A Support-vector Machine (Svm)mentioning

confidence: 99%

See 1 more Smart Citation

Authentication and Authorization for Mobile IoT Devices Using Biofeatures: Recent Advances and Future Trends

Ferrag

Μαγλαράς

Derhab

2019

Security and Communication Networks

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Bio-features are fast becoming a key tool to authenticate the IoT devices; in this sense, the purpose of this investigation is to summaries the factors that hinder biometrics models' development and deployment on a large scale, including human physiological (e.g., face, eyes, fingerprints-palm, or electrocardiogram) and behavioral features (e.g., signature, voice, gait, or keystroke). The different machine learning and data mining methods used by authentication and authorization schemes for mobile IoT devices are provided. Threat models and countermeasures used by biometrics-based authentication schemes for mobile IoT devices are also presented. More specifically, We analyze the state of the art of the existing biometric-based authentication schemes for IoT devices. Based on the current taxonomy, We conclude our paper with different types of challenges for future research efforts in biometrics-based authentication schemes for IoT devices.

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“…Authentication [3] also allows accountability based on the possibility of mapping the access link and concurrent actions to identities. The techniques of authentication are classified into three essential categories which are token-based authentication, biometric based authentication [4] and knowledge-based authentication system [5]. Fig.…”

Section: Introductionmentioning

confidence: 99%

Knowledge based Authentication Techniques and Challenges

Alhakami¹,

ShouqAlhrbi²

2020

IJACSA

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Knowledge-based Authentication (KBA) is an authentication approach, which verifying the user identity when accessing services such as finical websites. KBA requests specific information to prove personal identity of the owner. This paper discusses the challenges that are faced by KBA techniques. Memorability is the main obstacle in KBA since the users trying to utilize simple passwords or unify the passwords in various services, a step that cause problems and issues with compliance with security policies. Furthermore, the technique of mixing username/password is considered as another important challenge of KBA due to the recall-based authentication. This discussion includes a comparative analysis of KBA's techniques based on trade-off criteria to support making of decision. This study's results can support organizations in the recommendations process of a suitable KBA technique for organizations.

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PrivBioMTAuth: Privacy Preserving Biometrics-Based and User Centric Protocol for User Authentication From Mobile Phones

Cited by 64 publications

References 25 publications

Chaotic-map based authenticated security framework with privacy preservation for remote point-of-care

Chaotic-map based authenticated security framework with privacy preservation for remote point-of-care

Authentication and Authorization for Mobile IoT Devices Using Biofeatures: Recent Advances and Future Trends

Knowledge based Authentication Techniques and Challenges

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