scholarly journals An Improved Fuzzy Vector Signature with Reusability

2020 ◽  
Vol 10 (20) ◽  
pp. 7141
Author(s):  
Ilhwan Lim ◽  
Minhye Seo ◽  
Dong Hoon Lee ◽  
Jong Hwan Park
Keyword(s):  
Sampling Method ◽  
Performance Comparison ◽  
Error Rates ◽  
Efficiency Improvement ◽  
Fuzzy Extractor ◽  
Authentication Protocols ◽  
Biometric Data ◽  
Single User ◽  
Helper Data ◽  
Fuzzy Vector

Fuzzy vector signature (FVS) is a new primitive where a fuzzy (biometric) data w is used to generate a verification key (VKw), and, later, a distinct fuzzy (biometric) data w′ (as well as a message) is used to generate a signature (σw′). The primary feature of FVS is that the signature (σw′) can be verified under the verification key (VKw) only if w is close to w′ in a certain predefined distance. Recently, Seo et al. proposed an FVS scheme that was constructed (loosely) using a subset-based sampling method to reduce the size of helper data. However, their construction fails to provide the reusability property that requires that no adversary gains the information on fuzzy (biometric) data even if multiple verification keys and relevant signatures of a single user, which are all generated with correlated fuzzy (biometric) data, are exposed to the adversary. In this paper, we propose an improved FVS scheme which is proven to be reusable with respect to arbitrary correlated fuzzy (biometric) inputs. Our efficiency improvement is achieved by strictly applying the subset-based sampling method used before to build a fuzzy extractor by Canetti et al. and by slightly modifying the structure of the verification key. Our FVS scheme can still tolerate sub-linear error rates of input sources and also reduce the signing cost of a user by about half of the original FVS scheme. Finally, we present authentication protocols based on fuzzy extractor and FVS scheme and give performance comparison between them in terms of computation and transmission costs.

scholarly journals Construction of a New Biometric-Based Key Derivation Function and Its Application

2018 ◽  
Vol 2018 ◽  
pp. 1-14
Author(s):  
Minhye Seo ◽  
Jong Hwan Park ◽  
Youngsam Kim ◽  
Sangrae Cho ◽  
Dong Hoon Lee ◽  
...  
Keyword(s):  
Authentication Protocol ◽  
Security Model ◽  
Computationally Efficient ◽  
Fuzzy Extractor ◽  
Biometric Data ◽  
Cryptographic Keys ◽  
Cryptographic Key ◽  
Biometric Cryptosystem ◽  
Helper Data ◽  
Key Derivation

Biometric data is user-identifiable and therefore methods to use biometrics for authentication have been widely researched. Biometric cryptosystems allow for a user to derive a cryptographic key from noisy biometric data and perform a cryptographic task for authentication or encryption. The fuzzy extractor is known as a prominent biometric cryptosystem. However, the fuzzy extractor has a drawback in that a user is required to store user-specific helper data or receive it online from the server with additional trusted channel, to derive a correct key. In this paper, we present a new biometric-based key derivation function (BB-KDF) to address the issues. In our BB-KDF, users are able to derive cryptographic keys solely from their own biometric data: users do not need any other user-specific helper information. We introduce a security model for the BB-KDF. We then construct the BB-KDF and prove its security in our security model. We then propose an authentication protocol based on the BB-KDF. Finally, we give experimental results to analyze the performance of the BB-KDF. We show that our proposed BB-KDF is computationally efficient and can be deployed on many different kinds of devices.

scholarly journals Transformed Secure Feed Forward Supervised Learning Method for Authentication in Multi-Model Biometric System

2019 ◽  
Vol 8 (9) ◽  
pp. 2293-2297
Keyword(s):  
Image Quality ◽  
Recognition Rate ◽  
Error Rates ◽  
Social Characteristics ◽  
Computational Time ◽  
Discrete Wavelet ◽  
Biometric System ◽  
Feed Forward Neural Network ◽  
Biometric Data ◽  
Feed Forward

Biometric is an automated detection of the characteristics of an individual on the basis of the biological and social features. Detection of the uni-modal biometric system is based on the biometric data of an individual. Some issue of distortion level spoofing threats are more accessible to biometric data. Some of the issues overcome by multimodal biometric scheme in which signature of the biometric data are determine for better security of the data. Multimodal biometric is used on variety of the application areas which are human computer interface, detection of the sensor through unique method. The physical and social characteristics are used for the identification of an individual using multimodal biometric system. Multi-model biometric system applications are security system developed in banking sectors, business phase and Industry (MNC) companies. In existing work, using ESVM method to recognize the biometric traits and problem occurs in existing phase is distortion and degrades the image quality present and reduces the recognition rate and high error rates. In proposed research, determined the biometric features finger print, face and iris through CASIA dataset. Then, distortion rate is recognised through salt and pepper method and removal of interference using filtration technique. After that, discrete wavelet transformation is used for the extraction of the features of the biometric system through face, fingerprint and eye that determine the graphical features. Along with that, feed forward neural network algorithm developed for classification and recognition of multi modal biometric behaviour characteristics. The Encrypted NN method conducts simulation work on the metrics like as a recognition rate, true positive rate and computation time. The experimental results demonstrate that Encrypted NN method is able to enhance the image quality, recognition rate and TPR and reduces the computational time of Multi-model Biometric System when compared with existing work and simulation tool used MATLAB 2016a.

scholarly journals A performance comparison of sampling methods in the assessment of species composition patterns and environment–vegetation relationships in species-rich grasslands

2017 ◽  
Vol 86 (4) ◽  
Author(s):  
Grzegorz Swacha ◽  
Zoltán Botta-Dukát ◽  
Zygmunt Kącki ◽  
Daniel Pruchniewicz ◽  
Ludwik Żołnierz
Keyword(s):  
Species Composition ◽  
Sampling Method ◽  
Sampling Methods ◽  
Vegetation Type ◽  
Habitat Type ◽  
Spatial Arrangement ◽  
Performance Comparison ◽  
Systematic Sampling ◽  
Soil Parameters ◽  
Preferential Sampling

The influence that different sampling methods have on the results and the interpretation of vegetation analysis has been much debated, but little is yet known about how the spatial arrangement of samples affect patterns of species composition and environment–vegetation relationships within the same vegetation type. We compared three data sets of the same sample size obtained by three standard sampling methods: preferential, random, and systematic. These different sampling methods were applied to a study area comprising of 36 ha of intermittently wet <em>Molinia</em> meadows. We compared the performance of the three methods under two management categories: managed (extensively mown) and unmanaged (abandoned for 10 years). A total of 285 vegetation-plots were sampled, with 95 plots recorded per sampling method. In preferential sampling, we sampled only patches of vegetation with an abundance of indicator species of the habitat type, while random and systematic plots were positioned independently from the researcher by using GIS. The effect of each sampling method on the patterns of species composition and species–environment relationships was explored by redundancy analysis and the significance of effects was tested by the randomization test. Preferential sampling revealed different patterns of species composition than random and systematic sampling methods. Random and systematic sampling methods have resulted in broader vegetation variability than with preferential sampling method. Preferential sampling revealed different relationship between soil parameters and species composition in contrast to random and systematic sampling methods. Although we have not found significant differences in vegetation–environment relationships between random and systematic sampling methods, random sampling revealed a more robust correlation of species data to soil factors than preferential and systematic sampling methods. Intentional restriction of vegetation variation sampled preferentially may be detrimental to statistical inference in studies of species composition patterns and vegetation–environment relationships.

scholarly journals Multibiometrics System Design Based on Feature Level Fusion

2019 ◽  
Vol 9 (1) ◽  
pp. 4127-4132
Keyword(s):  
Hamming Distance ◽  
Research Work ◽  
Recognition Rate ◽  
Error Rates ◽  
Discrete Wavelet ◽  
Biometric Data ◽  
Multimodal System ◽  
Extraction Algorithm ◽  
Authentication System ◽  
Biometric Information

An efficient multimodal biometric system which combines biometric data originated from face, iris and signature biometrics has been presented. Proposed feature extraction algorithm for unimodal and multimodal system has been based on discrete wavelet transform. Among the various biometrics face and iris based human authentication system are proved reliable and efficient. Signature as a behavioral biometrics is very important in financial transaction. Signature has highest variability among all biometrics. This research work proposes an approach to combine signature biometrics with face and iris biometric. Proposed method fuses biometric information originated from face, iris and signature at feature level. Hamming distance based classifier has been used for classifying feature vector as a genuine or imposter. Proposed multibiometrics system has been evaluated on chimeric databases. It has been shown by the reported results that proposed multimodal system outperforms unimodal system performance. Proposed system has been analyzed for recognition rates and error rates. Performance of proposed multimodal system shows improvement in recognition rate and reduction in error

scholarly journals A New Scheme for the Polynomial Based Biometric Cryptosystems

2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Amioy Kumar ◽  
M. Hanmandlu ◽  
Hari M. Gupta
Keyword(s):  
Feature Extraction ◽  
Gabor Filter ◽  
Error Rates ◽  
Experimental Results ◽  
Feature Points ◽  
Secret Key ◽  
Biometric Data ◽  
Fuzzy Vault ◽  
Palm Vein ◽  
Ordered Pairs

This paper presents a new scheme for the fuzzy vault based biometric cryptosystems which explore the feasibility of a polynomial based vault for the biometric traits like iris, palm, vein, and so forth. Gabor filter is used for the feature extraction from the biometric data and the extracted feature points are transformed into Eigen spaces using Karhunen Loeve (K-L) transform. A polynomial obtained from the secret key is used to generate projections from the transformed features and the randomly generated points, known as chaff points. The points and their corresponding projections form the ordered pairs. The union of the ordered pairs from the features and the chaff points creates a fuzzy vault. At the time of decoding, matching scores are computed by comparing the stored and the claimed biometric traits, which are further tested against a predefined threshold. The number of matched scores should be greater than a tolerance value for the successful decoding of the vault. The threshold and the tolerance value are learned from the transformed features at the encoding stage and chosen according to the tradeoff in the error rates. The proposed scheme is tested on a variety of biometric databases and error rates obtained from the experimental results confirm the utility of the new scheme.

scholarly journals Proposed Method to Generated Strong Keys by Fuzzy Extractor And Biometric

2018 ◽  
Vol 7 (3.27) ◽  
pp. 129 ◽  
Author(s):  
Huda Saleem ◽  
Huda Albermany ◽  
Husein Hadi
Keyword(s):  
Error Correction ◽  
Error Control ◽  
Forward Error Correction ◽  
Error Correcting Codes ◽  
Fuzzy Extractor ◽  
Biometric Data ◽  
Convolutional Coding ◽  
Noise Data ◽  
Typical Scheme ◽  
Forward Error

The typical scheme used to generated cryptographic key is a fuzzy extractor. The fuzzy extractor is the extraction of a stable data from biometric data or noisy data based on the error correction code (ECC) method. Forward error correction includes two ways are blocked and convolutional coding used for error control coding. “Bose_Chaudhuri_Hocquenghem” (BCH) is one of the error correcting codes employ to correct errors in noise data. In this paper use fuzzy extractor scheme to find strong key based on BCH coding, face recognition data used SVD method and hash function. Hash_512 converted a string with variable length into a string of fixed length, it aims to protect information against the threat of repudiation.  

scholarly journals Classification of Large Biometric Data in Database System

2021 ◽  
Vol 10 (10) ◽  
pp. 1-8
Author(s):  
Varisha Alam* ◽  
◽  
Dr. Mohammad Arif ◽  
Keyword(s):  
Recovery Time ◽  
Database System ◽  
Error Rates ◽  
Small Data ◽  
Biometric Data ◽  
Data Set ◽  
Main Motivation ◽  
Information Recovery ◽  
Life Estimation

"Biometrics" is got from the Greek word 'life' and 'measure' which implies living and evaluation take apart. It simply converts into "life estimation". Biometrics uses computerized acknowledgment of people, dependent on their social and natural attributes. Biometric character are data separated from biometric tests, which can use for examination with a biometric orientation. Biometrics involves techniques to unusually recognize people dependent on at least one inherent physical or behavior attribute. In software engineering, specifically, biometric is used as a form of character retrieve the Committee and retrieve command. Biometric identically utilized to recognize people in bunches that are in observation. Biometric has quickly risen like a auspicious innovation for validation and has effectively discovered a spot in most of the scientific safety regions. An effective bunching method suggest for dividing enormous biometrics data set through recognizable proof. This method depends on the changed B+ tree is decreasing the discs get to. It diminishes the information recovery time and also possible error rates. Hence, for bigger applications, the need to reduce the data set to a more adequate portion emerges to accomplish both higher paces and further developed precision. The main motivation behind ordering is to recover a small data set for looking through the inquiry

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