Comparison of BE and CB in .NET

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Comparison of BE and CB
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Both BE and CB satisfy the requirements for untraceable biometrics, since the biometric image or template is transformed to a domain from which it cannot be recovered; a large number of those transforms exists; the transformation is application-dependent; and the resulting BE or CB template is cancelable (revocable). In general, BE and CB face similar technological challenges. First, the inherent variability of biometric samples makes it more dif cult for both BE and CE to achieve good accuracy than in conventional biometrics. Since BE usually operates in a blind mode (i.e., the biometric template is not seen on veri cation, unlike CB), this could be more technologically challenging in the case of BE, although evidence so far does not show signi cant advantages of CB in terms of accuracy. Additionally, besides improving the system FAR/FRR performance, another challenge is making the BE or CB template secure that is, resilient to of ine attacks. These issues are discussed in more detail in Section 26.6. It is important to note the following key differences between BE and CB: (a) CB: A distorted template is stored on enrollment. BE: A biometrically encrypted key, also called helper data, is stored. (b) CB: Distorting transform should be kept secret. BE: The cryptographic key is not kept at all; distorting transform is optional. (c) CB: Fresh distorted template is compared against stored distorted template. BE: Undistorted biometric is applied to the stored biometrically encrypted key. (d) CB: Binary Yes/No response on veri cation. BE: The veri cation output is either a key or a failure message.
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Biometric Encryption: The New Breed of Untraceable Biometrics
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Table 26.1. Comparison of the Performance, Privacy, and Security Aspects of BE, CB, and Conventional Biometrics Based on the Perception of the Authors.a Conventional Biometrics Accuracy Speed Untraceability User s control and trust Prevention of secondary uses and abuses Revocability Resilience to spoo ng Resilience to substitution attack and tampering Resilience to masquerade attack Resilience to Trojan horse attack Resilience to overriding Yes/No response Integration into conventional cryptosystem
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Cancelable Biometrics
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(e) CB: Closer to a conventional biometric system (even the same matching algorithm can be used in some CB schemes [14]). BE: Can be integrated with a conventional cryptosystem. (f) CB: Can be attacked by overriding Yes/No response, by a Trojan horse attack, and by a substitution attack. BE: Is immune against those attacks. Table 26.1 compares BE, CB, and conventional biometrics in terms of performance, privacy, and security. As illustrated in Table 26.1, both BE and CB provide overall better security and privacy protection than conventional biometrics. However, it is also shown that of the two UB technologies, BE is potentially the most secure and privacy protective. We will proceed to explain in further detail why, in our view, BE is the technology of choice for building privacy into biometric systems.
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Biometric Encryption at a Glance
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The original concept of biometric encryption for ngerprints was pioneered in 1994 by G. Tomko, founder of Mytec Technologies (Toronto, Canada). Since then, many research groups have published their work on developing BE and related technologies as re ected in the review papers by Uludag et al. [27] and Jain et al. [28] and in the book edited by Tuyls, Skori , and Kevenaar [29]. c The most distinct BE technologies are the following: Mytec1 and Mytec2, ECC check bits, biometrically hardened passwords, the fuzzy commitment scheme and some of its generalizations in the fuzzy extractor/secure sketch framework, shielding
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26.3 Introduction to Untraceable Biometrics (UB)
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functions (i.e., quantization using correction vector), fuzzy vault, PinSketch, and BioHashing with key binding. Most other works are close to one of those groups. There are two BE approaches: key generation, when a key is derived from a biometric, and key binding, when an arbitrary key (e.g., randomly generated) is securely bound to the biometric. Both approaches store biometric-dependent helper data. Some BE schemes (e.g., the fuzzy commitment or the fuzzy vault schemes) can equally work in both key generation and key binding mode; the key generation is also called secure sketch [30]. The latter implies that the enrolled biometric template will be recovered on veri cation when a fresh biometric sample is applied to the helper data (i.e., the enrolled template itself, or a string derived from it, is a key). This key is biometric-dependent rather than arbitrary, and the size of the key space is unknown. In this section, we will focus primarily on the key binding approach. As illustrated in Figure 26.2, the digital key (password, PIN, etc.) is randomly generated on enrollment, so that neither the user, nor anybody else, knows it. The key
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