A dissection of fingerprint fuzzy vault schemes

Krivokuća, Vedrana; Abdulla, Waleed H.; Swain, Akshya

doi:10.1145/2425836.2425888

Cited by 6 publications

(4 citation statements)

References 23 publications

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“…The vault can only be unlocked if there is another set of data which significantly overlaps with the original data. The generic fuzzy vault is suitable for unordered feature sets (such as fingerprints) and does not suffer performance degradation due to missing or spurious feature elements in multiple acquisitions of the same biometric data [49]. Fuzzy vault has been widely used to protect stored templates in automatic fingerprint verification systems [50,51].…”

Section: Fuzzy Vaultmentioning

confidence: 99%

State of the Art in Biometric Key Binding and Key Generation Schemes

Jegede

Udzir

Abdullah

et al. 2022

Int. j. commun. netw. inf. secur.

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Direct storage of biometric templates in databases exposes the authentication system and legitimate users to numerous security and privacy challenges. Biometric cryptosystems or template protection schemes are used to overcome the security and privacy challenges associated with the use of biometrics as a means of authentication. This paper presents a review of previous works in biometric key binding and key generation schemes. The review focuses on key binding techniques such as biometric encryption, fuzzy commitment scheme, fuzzy vault and shielding function. Two categories of key generation schemes considered are private template and quantization schemes. The paper also discusses the modes of operations, strengths and weaknesses of various kinds of key-based template protection schemes. The goal is to provide the reader with a clear understanding of the current and emerging trends in key-based biometric cryptosystems.

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Section: Fuzzy Vaultmentioning

confidence: 99%

State of the Art in Biometric Key Binding and Key Generation Schemes

Jegede

Udzir

Abdullah

et al. 2022

Int. j. commun. netw. inf. secur.

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“…Building upon this preliminary analysis, a series of implementations for minutiae-based fingerprint fuzzy vaults was proposed [57], [58]. A useful guide for constructing a fuzzy vault scheme is provided in [59]. Different security analyses have found that the original fuzzy vault scheme is vulnerable to a certain kind of linkage attack, i.e.…”

Section: Fuzzy Vault Schemementioning

confidence: 99%

Deep Face Fuzzy Vault: Implementation and Performance

Rathgeb¹,

Merkle²,

Scholz³

et al. 2021

Preprint

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Deep convolutional neural networks have achieved remarkable improvements in facial recognition performance. Similar kinds of developments, e.g. deconvolutional neural networks, have shown impressive results for reconstructing face images from their corresponding embeddings in the latent space. This poses a severe security risk which necessitates the protection of stored deep face embeddings in order to prevent from misuse, e.g. identity fraud.In this work, an unlinkable improved deep face fuzzy vaultbased template protection scheme is presented. To this end, a feature transformation method is introduced which maps fixedlength real-valued deep face embeddings to integer-valued feature sets. As part of said feature transformation, a detailed analysis of different feature quantisation and binarisation techniques is conducted using features extracted with a state-of-the-art deep convolutional neural network trained with the additive angular margin loss (ArcFace). At key binding, obtained feature sets are locked in an unlinkable improved fuzzy vault. For key retrieval, the efficiency of different polynomial reconstruction techniques is investigated. The proposed feature transformation method and template protection scheme are agnostic of the biometric characteristic and, thus, can be applied to virtually any biometric features computed by a deep neural network.For the best configuration, a false non-match rate below 1% at a false match rate of 0.01%, is achieved in cross-database experiments on the FERET and FRGCv2 face databases. On average, a security level of up to approximately 28 bits is obtained. This work presents the first effective face-based fuzzy vault scheme providing privacy protection of facial reference data as well as digital key derivation from face.

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“…Hash functions are employed in our construction to further protect each single biometric trait. A dissection of fingerprint fuzzy vault schemes" the university of auckland new zealand, [4] vedrana krivokuća, waleed abdulla, akshya swai 2014. In this paper the fuzzy vault construction is adopted for the protection of fingerprint data.An alignment-free fingerprint cryptosystem based on fuzzy vault scheme, Journal of network and computer applications, peng li , xinyang , kaicao , xunqiangtao, ruifangwang , jietian 33 (2010) 207-220 [1].…”

Section: IImentioning

confidence: 99%

Secured Fingerprint based Crypto System with Reversible Watermarking Scheme

R¹

2017

IJRASET

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As the growth of technology every information is passed widely through the internet. To ensure the secure data transmission, cryptography is the most effective solution. Cryptographic key plays an important entity to overcome channel attacks. High security is difficult in randomly generated cryptographic keys. Here random key needs to be stored in a protected place or it must transported through a shared communication line. As an alternative to this, the generation of finger print based key using the biometric information of sender/receiver is introduced. Thus avoiding key storing and at the same time without compromising the strength in security. Biometric based cryptographic key generation has some difficulties to maintain privacy of biometrics and key generation at receiver in present error data. Biometric key is generated from matching process that enrolls highly secured cryptosystem. Implementation of this proposed work has been analyzed using reversible watermarking scheme.

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A dissection of fingerprint fuzzy vault schemes

Cited by 6 publications

References 23 publications

State of the Art in Biometric Key Binding and Key Generation Schemes

State of the Art in Biometric Key Binding and Key Generation Schemes

Deep Face Fuzzy Vault: Implementation and Performance

Secured Fingerprint based Crypto System with Reversible Watermarking Scheme

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