Biometric Identification Systems with Noisy Enrollment for Gaussian Sources and Channels

Yachongka, Vamoua; Yagi, Hideki; Oohama, Yasutada

doi:10.3390/e23081049

Cited by 8 publications

(8 citation statements)

References 33 publications

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“…Also, it was demonstrated that the strong secrecy for secrecy-leakage of SKA systems is achievable by information-spectrum methods [22]. For future work, we plan to clarify the optimal tradeoff for GS and CS models with noisy enrollment as in [24], [25] and vector Gaussian sources as in [26].…”

Section: Discussionmentioning

confidence: 99%

Secret Key-based Authentication With Passive Eavesdropper for Scalar Gaussian Sources

Yachongka¹,

Yagi²,

Oohama³

2022

Preprint

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We analyze the fundamental trade-off of secret keybased authentication systems in the presence of an eavesdropper for correlated Gaussian sources. A complete characterization of trade-off among secret-key, storage, and privacy-leakage rates of both generated and chosen secret models is provided. One of the main contributions is revealing that unlike the known results for discrete sources, there is no need for the second auxiliary random variable in characterizing the capacity regions for the Gaussian cases. In addition, it is shown that the strong secrecy for secrecy-leakage of the systems can be achieved by an informationspectrum approach, and the parametric expressions (computable forms) of the capacity regions are also derived.

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Section: Discussionmentioning

confidence: 99%

Secret Key-based Authentication With Passive Eavesdropper for Scalar Gaussian Sources

Yachongka¹,

Yagi²,

Oohama³

2022

Preprint

Self Cite

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“…where ⊕ k denotes the addition modulo-|P k | defined in (43). The helper data j k is shared with the CEO for reconstructing the source signal.…”

Section: Agents (Encoding)mentioning

confidence: 99%

“…Proof: The above lemma follows by the fact that the latter half of the helper data, i.e., S k ⊕ k P k , leaks no information about the source identifiers since the index S k is completely masked by the private key P k . The readers may refer to the analysis of privacy leakage in [5], [43] for a detailed discussion.…”

Section: Lemma 1 It Holds Thatmentioning

confidence: 99%

The Secure CEO Problem With Physical Identifiers Under Logarithmic Loss and Quadratic Distortion Measures

Yachongka¹,

Yagi²,

Ochiai³

2023

Preprint

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In this paper, we investigate the fundamental limits of the chief executive officer (CEO) problem in which physical and biometric identifiers are treated as information sources. In order to make the information leakage of the identifiers to the eavesdropper via helper data negligible, private keys, uniformly and independently chosen, are bonded into measurements of the identifiers at the encoders to generate the helper data. The CEO problem is renowned for the difficulty of characterizing the tight rate-distortion region, which is still an open question for the general case. In this study, we characterize the tight rate-key-distortion regions of such problem under two specific distortion measures, namely, logarithmic loss (both discrete and Gaussian settings) and quadratic distortion measures. Also, we provide numerical calculations of the characterized regions, and the calculated results show that when a larger distortion is permitted, smaller storage and private-key rates are achievable. As special cases where the constraints of private-key rates and negligible leakage are not imposed, our characterizations naturally reduce to the rate-distortion regions provided by Courtade and Weissman (2014) for logarithmic loss distortion, and Prabhakaran et al. (2004), Chen et al. (2004), and Oohama (2005 for quadratic distortion measure.

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“…A plethora of human anatomical features have been used for biometric systems. As presented in Figure 1, some of these include the use of fingerprints, face, iris, retina, voice, handwritten, face thermogram, hand geometry recognition, palm vein, and DNA matching [2]- [15]. In facial recognition systems (Fig.…”

Section: Introductionmentioning

confidence: 99%

A GUI-Based Peg-Free Hand Geometry Recognition for Biometric Access Control using Artificial Neural Network

Adedeji

Esan

2022

J. Eng. Adv.

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Hand geometry has been a widely used biometric authentication because it is generally believed that the human hand has sufficient anatomical features which could be used for personal identification. Many hand geometry systems use pegs, which guide hand placement on the scanner. The system prompts the user to position the hand on the scanner several times and only captures when the current position is satisfied. In such a system, measurements are not very precise and this reduces accuracy during feature extraction. The system also has a higher false acceptance rate. This paper presents a peg-free hand geometry recognition system that does not depend on the orientation of the hand. Several features from test hand images are extracted and stored in the database, which are used to train an artificial neural network (ANN). To facilitate easy usage of the hand geometry verification system (peg-free), a GUI was developed using MATLAB software. The developed system was validated and the overall result shows that the system can be used for biometric verification using hand geometry where the orientation and placement of the hand are not a necessity. The results show that the developed system performed better with a relatively low false acceptance rate and false rejection rate of 0.01% and 0.02% respectively. The system also has a lower mean square error of 8.84×10-5.

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Biometric Identification Systems with Noisy Enrollment for Gaussian Sources and Channels

Cited by 8 publications

References 33 publications

Secret Key-based Authentication With Passive Eavesdropper for Scalar Gaussian Sources

Secret Key-based Authentication With Passive Eavesdropper for Scalar Gaussian Sources

The Secure CEO Problem With Physical Identifiers Under Logarithmic Loss and Quadratic Distortion Measures

A GUI-Based Peg-Free Hand Geometry Recognition for Biometric Access Control using Artificial Neural Network

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