865 research outputs found
A New Biometric Template Protection using Random Orthonormal Projection and Fuzzy Commitment
Biometric template protection is one of most essential parts in putting a
biometric-based authentication system into practice. There have been many
researches proposing different solutions to secure biometric templates of
users. They can be categorized into two approaches: feature transformation and
biometric cryptosystem. However, no one single template protection approach can
satisfy all the requirements of a secure biometric-based authentication system.
In this work, we will propose a novel hybrid biometric template protection
which takes benefits of both approaches while preventing their limitations. The
experiments demonstrate that the performance of the system can be maintained
with the support of a new random orthonormal project technique, which reduces
the computational complexity while preserving the accuracy. Meanwhile, the
security of biometric templates is guaranteed by employing fuzzy commitment
protocol.Comment: 11 pages, 6 figures, accepted for IMCOM 201
Relations among Security Metrics for Template Protection Algorithms
Many biometric template protection algorithms have been proposed mainly in
two approaches: biometric feature transformation and biometric cryptosystem.
Security evaluation of the proposed algorithms are often conducted in various
inconsistent manner. Thus, it is strongly demanded to establish the common
evaluation metrics for easier comparison among many algorithms. Simoens et al.
and Nagar et al. proposed good metrics covering nearly all aspect of
requirements expected for biometric template protection algorithms. One
drawback of the two papers is that they are biased to experimental evaluation
of security of biometric template protection algorithms. Therefore, it was
still difficult mainly for algorithms in biometric cryptosystem to prove their
security according to the proposed metrics. This paper will give a formal
definitions for security metrics proposed by Simoens et al. and Nagar et al. so
that it can be used for the evaluation of both of the two approaches. Further,
this paper will discuss the relations among several notions of security
metrics
Homomorphic Encryption for Speaker Recognition: Protection of Biometric Templates and Vendor Model Parameters
Data privacy is crucial when dealing with biometric data. Accounting for the
latest European data privacy regulation and payment service directive,
biometric template protection is essential for any commercial application.
Ensuring unlinkability across biometric service operators, irreversibility of
leaked encrypted templates, and renewability of e.g., voice models following
the i-vector paradigm, biometric voice-based systems are prepared for the
latest EU data privacy legislation. Employing Paillier cryptosystems, Euclidean
and cosine comparators are known to ensure data privacy demands, without loss
of discrimination nor calibration performance. Bridging gaps from template
protection to speaker recognition, two architectures are proposed for the
two-covariance comparator, serving as a generative model in this study. The
first architecture preserves privacy of biometric data capture subjects. In the
second architecture, model parameters of the comparator are encrypted as well,
such that biometric service providers can supply the same comparison modules
employing different key pairs to multiple biometric service operators. An
experimental proof-of-concept and complexity analysis is carried out on the
data from the 2013-2014 NIST i-vector machine learning challenge
Pitfall of the Detection Rate Optimized Bit Allocation within template protection and a remedy
One of the requirements of a biometric template protection system is that the protected template ideally should not leak any information about the biometric sample or its derivatives. In the literature, several proposed template protection techniques are based on binary vectors. Hence, they require the extraction of a binary representation from the real- valued biometric sample. In this work we focus on the Detection Rate Optimized Bit Allocation (DROBA) quantization scheme that extracts multiple bits per feature component while maximizing the overall detection rate. The allocation strategy has to be stored as auxiliary data for reuse in the verification phase and is considered as public. This implies that the auxiliary data should not leak any information about the extracted binary representation. Experiments in our work show that the original DROBA algorithm, as known in the literature, creates auxiliary data that leaks a significant amount of information. We show how an adversary is able to exploit this information and significantly increase its success rate on obtaining a false accept. Fortunately, the information leakage can be mitigated by restricting the allocation freedom of the DROBA algorithm. We propose a method based on population statistics and empirically illustrate its effectiveness. All the experiments are based on the MCYT fingerprint database using two different texture based feature extraction algorithms
Multi-sample fusion with template protection
The widespread use of biometries and its increased popularity introduces privacy risks. In order to mitigate these risks, solutions such as the helper-data system, fuzzy vault, fuzzy extractors, and cancelable biometries were introduced, also known as the field of template proteetion. Besides these developments, fusion of multiple sources of biometrie information have shown to improve the verification performance of the biometrie system. Our work eonsists of analyzing feature-level fusion in the context of the template proteetion framework using the helper-data system. We verify the results using the FRGC v2 database and two feature extraction algorithms
Template Protection For 3D Face Recognition
The human face is one of the most important biometric modalities for automatic authentication. Three-dimensional face recognition exploits facial surface information. In comparison to illumination based 2D face recognition, it has good robustness and high fake resistance, so that it can be used in high security areas. Nevertheless, as in other common biometric systems, potential risks of identity theft, cross matching and exposure of privacy information threaten the security of the authentication system as well as the user\\u27s privacy. As a crucial supplementary of biometrics, the template protection technique can prevent security leakages and protect privacy. In this chapter, we show security leakages in common biometric systems and give a detailed introduction on template protection techniques. Then the latest results of template protection techniques in 3D face recognition systems are presented. The recognition performances as well as the security gains are analyzed
A hybrid biometric template protection algorithm in fingerprint biometric system
Biometric recognition has achieved a considerable popularity in recent years due its various properties and widespread application in various sectors. These include very top priority sectors like countries boundary security, military, space missions, banks etc. Due to these reasons the stealing of biometric information is a critical issue. To protect this user biometric template information there should be efficient biometric template transformation technique and thereby the privacy of user is preserved. Non-invertible transformation can keep the user template based transformed information maximum secure against the regeneration. But the performance of non-invertible template protection mechanism will be reduced by the increase in security. This limitation of non-invertible biometric transformation should be solved. This research aims to develop a hybrid biometric template protection algorithm to keep up a balance between security and performance in fingerprint biometric system. The hybrid biometric template protection algorithm is developed from the combination of non-invertible biometric transformation and biometric key generation techniques. To meet the research objective this proposed framework composed of three phases: First phase focus on the extraction of fingerprint minutiae and formation of vector table, while second phase focus on develop a hybrid biometric template protection algorithm and finally the third phase focus on evaluation of performance of the proposed algorithm
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