Figure 26.2. High-level diagram of a biometric encryption process. (a) Enrollment; (b) Veri cation. in .NET

Paint qr codes in .NET Figure 26.2. High-level diagram of a biometric encryption process. (a) Enrollment; (b) Veri cation.
Figure 26.2. High-level diagram of a biometric encryption process. (a) Enrollment; (b) Veri cation.
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Biometric Encryption: The New Breed of Untraceable Biometrics
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itself is completely independent of biometrics and, therefore, can always be changed or updated. After a biometric sample is acquired, the BE algorithm securely and consistently binds the key to the biometric to create a protected BE template, also called helper data, biometrically encrypted key, virtual PIN, private template, and so on. In essence, the key is encrypted with the biometric. The BE template provides privacy protection and can be stored either in a database or locally (smart card, token, laptop, cell phone, etc.). At the end of the enrollment, both the key and the biometric are discarded. On veri cation, the user presents her fresh biometric sample, which, when applied to the legitimate BE template, will let the BE algorithm retrieve the same key. In other words, the biometric decrypts the key. At the end of veri cation, the biometric sample is discarded once again. The BE algorithm is designed to account for acceptable variations in the input biometric. On the other hand, an attacker whose biometric sample is different enough will not be able to retrieve the key. This encryption/decryption scheme is fuzzy, because the biometric sample is different each time, unlike an encryption key in conventional cryptography. Of course, this presents a big technological challenge to make the system work. After the digital key (or password, PIN, and so on) is retrieved, it can be used as the basis for any physical or logical application. The most obvious use is in the conventional cryptosystem, such as a PKI, where the password will generate a pair of public and private keys. In order to improve the security of BE system, an optional transform (shown in the dashed square in Figure 26.2) may be applied. Preferably, the transform should be noninvertible and kept secret. One of the ways would be employing a randomization technique, such as biohashing or salting in more general terms [14, 28]. The best approach would be to control the transform with the user s password. It can also be stored on a token or server, always separately from the rest of helper data. This is the same approach as employed by CB. It should be noted, however, that BE, unlike CB, does not rely on the secrecy of the transform. If properly implemented, BE is an effective, secure, and privacy-friendly tool for biometric key management, since the biometric and the key are bound on a fundamental level. In order to have a better understanding of how BE works, let us consider a relatively simple yet real-life example, a fuzzy commitment scheme for iris [31]. The standard iris template is an ordered string of 2048 bits. As shown in Figure 26.3, a 140-bit key is generated randomly and bound to the template on enrollment. This is done through an error correcting code (ECC), which is an important part of most BE algorithms. ECCs are typically used in communications, data storage, and in other systems where errors can occur [32, 33], with BE being a new area for the application of ECC. An (n, k, d) binary block ECC encodes k bits with n > k bits by adding some redundancy. Those n-bit strings are called codewords; there are 2k of them in total. The minimum distance (usually a Hamming distance is implied) between the codewords is d. If, at a later stage (in case of BE, on veri cation), the errors occur, the ECC is guaranteed to correct up to (d-1)/2 random bit errors among n bits.
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26.3 Introduction to Untraceable Biometrics (UB)
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Figure 26.3. Binding of a 140-bit key to a 2048-bit iris template in a fuzzy commitment scheme.
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In our example with iris, the ECC is designed to encode 140 bits into 2048-bit codeword. The redundancy rate of such ECC is quite high; 2048/140 = 14.63. On enrollment, the codeword is simply XOR-ed with the iris template, and the resulting biometrically encrypted key is stored. Neither the codeword nor the biometric template can be retrieved from the helper data, which is similar to a one-time-pad cryptosystem known in cryptography. It is interesting to note that there are no speci c locations where the 140-bit key is hidden; it is dispersed over all 2048 bits. On veri cation, a fresh 2048-bit iris template is obtained. Some bits may have errors. The fresh template is XOR-ed with the stored biometrically encrypted key. If there were no errors, the original codeword would be obtained. However, since errors are unavoidable in biometrics, the result of the XOR will differ from the correct codeword. Here the ECC decoder comes into play: If the number of errors is not too large, the ECC can correct all the errors and obtain the original codeword. Since the codewords are deterministically mapped to 140-bit keys, the correct 140-bit key will be retrieved. If, on the other hand, the number of errors exceeds the ECC s capability, the decoder will declare a failure. Therefore, the output of BE algorithm is either a key or a failure message. Ideally, the failure should be output for an impostor only; however, it could happen for a legitimate user as well that is, the system could have a false rejection, as in conventional biometrics. As we can see, in the case of BE the ECC replaces a simple threshold-based Yes/No scheme of conventional biometrics. Designing a good (2048, 140) ECC for BE is itself a serious technological challenge, since the error rate for a biometric template is usually high. Hao et al. [31] used a combination of Hadamard (aka 1st order Reed Muller) and Reed Solomon ECCs. Normally, a block ECC corrects up to 25% of errors in a hard decoding mode, which would be 511 errors in our example. However, the authors ran the Reed Muller ECC in a soft decoding mode (i.e., the decoder always outputs the nearest codeword, even in the case of possible failure), which allowed it to achieve better error-correcting capabilities. To make sure that the algorithm always outputs the correct key (e.g., in the soft decoding mode the ECC may output any key), a hashed value of the key is stored into the helper data, as shown in Figure 26.3. One-way hash functions are a standard
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