An Exemplifying Scheme in .NET

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25.5 An Exemplifying Scheme
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As a matter of fact, the enhancements of the sensors and of the hardware/software architectures associated with the reduction of the system costs will produce a growing interest and diffusion of the multimodal systems in the market. The application of proper, practical, and standard privacy-compliant guidelines is becoming more and more necessary.
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Building over the considerations of the previous sections, we describe the design of a multimodal veri cation scheme satisfying much of the discussed issues regarding privacy compliance. The discussion will point out how few biometric traits might be used to construct an identi cation code for a subject while still ensuring protection to the biometric templates themselves. Also, it will clarify a few problematic aspects that might be faced when constructing an actual implementation. A typical multimodal biometric veri cation scheme provides two basic modules. The rst, the enroll module, creates some sort of ID linked to a single user starting from the user s biometric samples. The ID could then be stored in, for example, a document or a smart card and must be provided during the veri cation phase. The second module, the veri cation one, veri es if the ID matches a new set of freshly provided biometrics. While the number of biometric traits might in principle be increased as desired, we limit the discussion to the case of two independent biometric readings.
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A Multimodal Enrollment Module
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At enrollment, as in common multimodal biometric systems, two different biometric readings are collected: for example, an iris scan of one eye and a ngerprint or the ngerprints from two different ngers. The samples are then processed using the feature extraction algorithms of choice, selected among what the market or the open literature offer. Each algorithm delivers a set of features depending on the biometric trait, which are then turned into a binary string. For example, concerning ngerprints, the features describe characteristic points of the ridges pattern; such numbers are then collected in what is called a binary template, possibly according to a standard. An example is the ANSI INCITS 378-2004 standard.1 Similarly, for iris, the image of the eye is processed to obtained a string of bits (the so-called iris code) directly. In a simpler multimodal biometric system, the two templates denoted with I1 and I2 (Figure 25.3) would be stored in a database or a portable ID. An attacher who could somehow access the database or recover the ID might obtain with little effort the templates of the user. To avoid such a scenario, the templates are generally encrypted using a public key infrastructure (thus relaying on, for example, a network). In here,
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National Standard for Information Technology X Finger Minutiae Format for Data Interchange.
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Privacy in Biometrics
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Figure 25.3. A multimodal biometric enroll module satisfying privacy compliance issues.
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following a different approach, the biometric strings are concealed exploiting their peculiar quality of being similar when obtained from the same subject. The novelty with respect to a multimodal biometric system begins in Figure 25.3 after the construction of I1 and I2 . First, I1 is fed into a fuzzy extractor. Fuzzy extractors are cryptographic primitives that enable the extraction of a random uniform string R from a given input in a noise-tolerant way. Therefore they convert a noisy nonuniform input, such as a biometric reading, into a easily and reliably reproducible binary string, allowing a certain degree of tolerance in the given input. The tolerance to variations within biometric strings is typically obtained using an [n, k, 2t + 1] errorcorrecting code, where n and k are the lengths of the codeword and the message, respectively, and t is the number of errors the code can correct. The code-correcting capability t needs to be large enough to compensate for within-subject variability in the biometric samples. On the other hand, it must be smaller that the between-subjects variability, or otherwise the tolerance of the fuzzy extractor might be so large that impostors might be recognized as genuine ID holders. But this is actually not a big issue in practice. In fact, usually the opposite problem arises and the error-correcting capability of typical codes is not large enough for practical applications involving biometric samples. Given the large inter-subjects variability of biometric templates, the fraction of errors the code must be able to withstand is larger than in usual ECC applications. Common ECC code, like BCH, are capable of correcting a fraction of errors n/t strictly < 0.25%, thus are often ruled out.2 Others binary codes might get closer to the t/n = 1/2 Singleton bound, but the Plotkin bound implies [37] that a binary code can correct more than n/4 errors only at the expenses of reducing the length of codeword to about log n. This is the route that one might pursue by deriving a binary code from a Reed Solomon code for which time-ef cient decoding routines exist.
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BCH codes could be employed in the schemes we suggested, but the construction needs to be generalized slightly. The main idea is that by injecting errors only over a restricted part of a longer codeword, a larger local error correction ratio is obtained, which in turn could easily satis es the requirements imposed by the biometrics at hand.
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