26.5 Overview of UB Technologies

decoding qr barcode on .netUsing Barcode Control SDK for .net vs 2010 Control to generate, create, read, scan barcode image in .net vs 2010 applications.

and EER = 0 (i.e., full separation) when the transform is kept secret. Basically, this work tries to integrate the CB approach of noninvertible transforms with BE. Such a scheme will likely be more secure against the nonrandomness and the reusability attacks (see Section 26.6). Kholmatov et al. [95] developed the ngerprint fuzzy vault as a secret sharing scheme that is, when a few users (or different ngers of the same user) can unlock the same secret. This can be done since the number of chaff points exceeds the number of real minutiae by at least one order of magnitude, so that the authors were able to enrol three users. However, this should increase the system FAR, which was not measured in reference 95 over a suf cient data sample. Nandakumar and Jain [156] developed the rst multibiometric fuzzy vault. 26.5.1.9 BioHashing (with Key Binding)

QR-Code barcode library for .netusing barcode printer for vs .net control to generate, create qr code jis x 0510 image in vs .net applications.

BioHashing is a technique that can be used both for CB and BE. It transforms the biometric feature set to a new space of a lower dimension by generating a random set of orthogonal vectors and obtaining an inner product between each vector and the biometric feature set. The result is binarized to produce a bit string. The random feature vectors are generated from a random seed that is kept secret for example, by storing it in a token. For CB, the resulting binary string is the transformed template. On veri cation, a new string is obtained using the same secret set of orthogonal vectors, and the Hamming distance is computed between two strings. The scheme can be applied to BE by adding one step of key binding. This is done via Shamir secret sharing with linear interpolation [96, 97], or within the framework of a standard fuzzy commitment scheme with Reed Solomon ECC [98] ( ngerprints and face). Both methods provide some error tolerance (although not very powerful). The authors report very good results: FRR = 0.93% at FAR = 0 (face recognition) [96], and FRR = 0.11 1.35% at FAR = 0 for ngerprints (FVC2002 databases were used) [42]. The latter results are better than those of the FVC2002 winner (i.e., among non-BE algorithms). However, those results were obtained simply because each impostor was assigned a different set of the secret random vectors, which made FAR arti cially equal to 0 (this is typical of a nonstolen token scenario; see discussion in Section 26.5.2). In other words, the good results are rather attributed to a secret nonbiometric component of the system. In general, the BioHashing approach (also called salting in more general terms [14]) of transforming the biometric feature set is promising in terms of improving the security of BE algorithm. 26.5.1.10 Graph-Based Coding

QR barcode library in .netUsing Barcode decoder for Visual Studio .NET Control to read, scan read, scan image in Visual Studio .NET applications.

In a series of recent publications by Martinian et al. and Draper et al. [99 101], a new approach based on modern advances in the theory of ECCs is presented. The authors use low-density parity check (LDPC) codes, which are the state-of-the-art channel codes. They seem to be quite suitable for BE purposes because r the LDPC codes can be designed as a single block (n, k) ECC with large numbers of n and k, which makes the system secure;

Barcode barcode library in .netusing vs .net crystal torender bar code in asp.net web,windows application

26

Bar Code barcode library in .netusing barcode integration for visual .net control to generate, create bar code image in visual .net applications.

Biometric Encryption: The New Breed of Untraceable Biometrics

Control qr image in visual c#using .net framework tobuild qr-code with asp.net web,windows application

r high error rates can be handled; r ef cient decoding algorithms are available; r LDPC coding is well represented graphically, which allows implementation of the graphical model movement. In other words, it is possible to model features of various biometrics for example, to accommodate minutiae distortions. In reference 99, the iris biometric is considered. First, unreliable bits are discarded from the iris template, which leaves 1806 bits available. Second, a random parity check matrix of the LDPC code is selected. Third, a LDPC syndrome is computed from the matrix and the biometric 1806-bit vector. This syndrome is stored as helper data. On veri cation, a fresh biometric template is applied to the syndrome. A belief propagation (BP) decoding algorithm is used. If the decoder succeeds (i.e., the number of error is within the decoder limit), the enrolled 1806-bit vector is recovered. The authors achieved good results for iris, with FRR varying from 0.1% to 10% and the system security varying from 50 to 110 bits, respectively. This ECC syndrome scheme falls under the de nition of secure sketch [30], which is the key generation technique. In references 100 and 101, an even more advanced technique is applied to ngerprint minutiae. The minutiae variability is modeled as movement, erasure, or insertion (i.e., spurious generation) of minutiae. This can be represented by a factor graph. At the same time, the graph is used to connect the biometric template to a LDPC syndrome. The scheme does not use minutiae angles as features (this is a potential area for future improvement). For the preliminary tests, the authors had to limit the number of enrolled minutiae in the range from 31 to 35 in order to maintain the system security; otherwise, the variable LDPC encoding rate should be applied to each template (a potential subject of future work). The FRR varies from 11.6% to 32.3% at corresponding FAR from 1% to 0.03%. The graph-based scheme, which is the most notable generalization of a fuzzy commitment (or its spinoff, the ECC syndrome [30]) scheme, seems to be quite secure and, in our opinion, is one of the most promising developments in the evolution of BE technologies. 26.5.1.11 Other Works

Aspx.net qr-codes implementwith .netgenerate, create qr code iso/iec18004 none on .net projects

The U.S. patent to Bjorn [102] describes a process that falls under the de nition of BE. It introduces an interesting idea: a number of ghost points (called chaff points in later works on fuzzy vault) are added to a ngerprint minutiae template to hide real minutiae. The ghost points are hashed to create a cryptographic key. However, the patent does not disclose the most important part that is, a method for differentiating the ghost points from real minutiae on veri cation. Burnett et al. [103] suggested using fuzzy extractors (or any other BE scheme) in a biometric identity-based signature scheme. A key string is generated from a biometric and then is used to create a public key and corresponding private key. One of the main applications of these schemes is in the area of nonrepudiation of documents.

Barcode 3 Of 9 printing on .netusing barcode generation for .net framework crystal control to generate, create 3 of 9 barcode image in .net framework crystal applications.

.net Framework ean-13 supplement 2 encodingfor .netuse visual studio .net ean-13 supplement 5 development toinsert european article number 13 with .net

GTIN - 13 printer on visual c#use aspx.net crystal upc - 13 generation toconnect ean13 for c#

Barcode Pdf417 reader in noneUsing Barcode Control SDK for None Control to generate, create, read, scan barcode image in None applications.

Control code-128 data for office wordto display code 128c and code-128c data, size, image with word barcode sdk

Control qr size on excel qr-code size on office excel