Biohashing for Securing Minutiae Template

Belguechi, Rima; Rosenberger, Christophe; Ait-Aoudia, Samy

doi:10.1109/icpr.2010.292

Cited by 30 publications

(23 citation statements)

References 3 publications

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“…For fingerprint texture template for example, the authors have demonstrated that the performance of the system in term of EER moves from 7.3% when no hashing is performed to 10.9% when basic BioHashing is operated under the hypothesis that the token is always stolen, while EER is evaluated to 1.5% in case where no TRN is stolen (FVC2002-DB2). These scores are also discussed in our papers (Belguechi, Rosenberger & Aoudia, 2010), (Belguechi, Hemery & Rosenberger, 2010) where different cases are considered, depending on whether the token is stolen or not.…”

Section: Discussionmentioning

confidence: 99%

“…The first work referencing the BioHashing technique is presented on face modality in (Goh & Ngo, 2003). Then the same technique has been declined to different modalities in the references (Teoh et al, 2004c), (Teoh et al, 2004a), (Connie et al, 2004) and more recently (Belguechi, Rosenberger & Aoudia, 2010), to mention just a few. Now, we detail the general principle of BioHashing.…”

Section: Biohashingmentioning

confidence: 99%

“…Both Principal Component Analysis (PCA) and Fisher Discriminant Analysis are at the core of PalmHashing developed in (Connie et al, 2004) from the ROI of the raw palmprint image. In two recent papers (Belguechi, Rosenberger & Aoudia, 2010), (Belguechi, Hemery & Rosenberger, 2010), we propose to extract biometric templates from minutiae representation, by using a Gabor filterbank, after a detection process of the region of interest.…”

Section: Biohashing Principlementioning

confidence: 99%

“…One can see that it is a two factor authentication protection scheme, in the sense that the transformation function combines a specific random number whose seed is stored in a token with the biometric feature expressed as a fixed-length vector F = ( f 1 ,..., f n ), F ∈ R n . Then BioHashing principle, detailed in (Belguechi, Rosenberger & Aoudia, 2010) for example, consists in the projection of the (normalized) biometric data on an orthonormal basis obtained from the random number. This first step somehow amounts to hide the biometric data in some space.…”

Section: Biohashing Principlementioning

confidence: 99%

See 3 more Smart Citations

An Overview on Privacy Preserving Biometrics

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et al. 2011

Recent Application in Biometrics

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Section: Discussionmentioning

confidence: 99%

Section: Biohashingmentioning

confidence: 99%

Section: Biohashing Principlementioning

confidence: 99%

Section: Biohashing Principlementioning

confidence: 99%

See 2 more Smart Citations

An Overview on Privacy Preserving Biometrics

Belguechi¹,

Cherrier²,

Alimi³

et al. 2011

Recent Application in Biometrics

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“…Nowadays, several protection schemes exist, but unfortunately not yet mature for large scale deployment. Examples of such schemes are fuzzy commitment [59], fuzzy vault scheme [60], and the BioHashing principle presented in several works by [61] and [62].…”

Section: Evaluation Of Privacy By Design Biometric Systemsmentioning

confidence: 99%

Evaluation of Biometric Systems

El-Abed¹,

Giot²,

Rosenberger³

2012

New Trends and Developments in Biometrics

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International audienceBiometrics is considered as a promising solution among traditional methods based on "what we own" (such as a key) or "what we know" (such as a password). It is based on "what we are" and "how we behave". Few people know that biometrics have been used for ages for identification or signature purposes. In 1928 for example, fingerprints were used for women clerical employees of Los Angeles police department as depicted in Figure 1. Fingerprints were also already used as a signature for commercial exchanges in Babylon (-3000 before JC). Alphonse Bertillon proposed in 1879 to use anthropometric information for police investigation. Nowadays, all police forces in the world use this kind of information to resolve crimes. The first prototypes of terminals providing an automatic processing of the voice and digital fingerprints have been defined in the middle of the years 1970. Nowadays, biometric authentication systems have many applications [1]: border control, e-commerce, etc. The main benefits of this technology are to provide a better security, and to facilitate the authentication process for a user. Also, it is usually difficult to copy the biometric characteristics of an individual than most of other authentication methods such as passwords. Despite the obvious advantages of biometric systems, their proliferation was not as much as attended. The main drawback is the uncertainty of the verification result. By contrast to password checking, the verification of biometric raw data is subject to errors and represented by a similarity percentage (100% is never reached). Others drawbacks related to vulnerabilities and usability issues exist. In addition, in order to be used in an industrial context, the quality of a biometric system must be precisely quantified. We need a reliable evaluation methodology in order to put into obviousness the benefit of a new biometric system. Moreover, many questions remain: Shall we be confident in this technology? What kind of biometric modalities can be used? What are the trends in this domain? The objective of this chapter is to answer these questions, by presenting an evaluation methodology of biometric systems

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Provably secure signature‐based anonymous user authentication protocol in an Internet of Things‐enabled intelligent precision agricultural environment

Vangala

Das

Lee

2021

Concurrency and Computation

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User authentication is a promising security solution in which an external user having his/her mobile device can securely access the real-time information directly from the deployed smart devices in an Internet of Things-enabled intelligent precision agricultural environment. To achieve this goal, we present a new signature-based three factor user authentication scheme. The established session key between a user and the accessed smart device is then used to make secure communication among them to fetch the real-time data of the device. A detailed security analysis including the random-oracle based formal security, formal security verification using the broadly recognized automated validation of internet security protocols and applications tool and nonmathematical informal security analysis show the robustness of the proposed scheme against a number of potential attacks. In addition, testbed experiments are performed for measuring computational time of various cryptographic primitives that are used for comparative study among the proposed scheme and other related existing competing schemes. The detailed comparative analysis shows that the proposed scheme has a better trade-off among its offered security and functionality features, and communication and computational overheads as compared with those for other competing schemes.

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Biohashing for Securing Minutiae Template

Cited by 30 publications

References 3 publications

An Overview on Privacy Preserving Biometrics

An Overview on Privacy Preserving Biometrics

Evaluation of Biometric Systems

Provably secure signature‐based anonymous user authentication protocol in an Internet of Things‐enabled intelligent precision agricultural environment

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