Biometric Encryption: The New Breed of Untraceable Biometrics in .NET

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Biometric Encryption: The New Breed of Untraceable Biometrics
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the greater the desired presence of one attribute (security), the lesser the presence of the opposing attribute (privacy). This perspective advances the view that adding privacy compromises system functionality, control, and effectiveness. Furthermore, it mistakenly believes that privacy interests can be satis ed by building system controls that seek to ensure the con dentiality and integrity of biometric data. In this environment, building true biometric privacy into an information system is seen principally as a cost, and rarely as an enhancement. This prevailing view posits that privacy can only be improved at the expense of security. The emerging area of privacy-enhancing biometric technologies, which we are referring to as untraceable biometrics, challenges this paradigm by making it possible to enhance both privacy and security in a positive-sum model. Protecting privacy should not lead to less security and more costly business practices. In our view, biometric encryption (BE) may be classi ed an untraceable biometric technology, deserving of closer investigation because it demonstrates superior privacy enhancing qualities. Engineering privacy directly into biometric systems through BE is not only possible, but highly desirable.
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26.2 THE CASE FOR BUILDING PRIVACY INTO BIOMETRIC TECHNOLOGIES 26.2.1 Security Vulnerabilities of Biometric Technologies
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Despite all of the emphasis on security in the development of biometric technologies, they, nonetheless, have some common security vulnerabilities. These give rise to risks that can have signi cant impacts on the reliability, trustworthiness, and usability of the entire information system and on the privacy and security interests of individuals. Security vulnerabilities of biometric systems include [1 3] (see Figure 26.1): r Spoo ng: Biometric systems can sometimes be fooled by applying fake biometrics such as ngerprints, or face or iris images. r Replay Attacks: Sensors can be circumvented by injecting a recorded image into the system input. r Substitution Attacks: The biometric template must be stored to allow user veri cation. If an attacker gets access to the storage, either local or remote, he/she can overwrite the legitimate user s template with his/her own in essence, stealing their identity. r Tampering: Feature sets on veri cation or in the templates can be modi ed in order to obtain a high veri cation score, no matter which image is presented to the system, or, alternatively, to bring the system down by making the score low for legitimate users. r Masquerade Attacks: It has been demonstrated that a digital artifact [4] image can be created from a ngerprint template, so that this artifact, if submitted
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26.2 The Case for Building Privacy into Biometric Technologies
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Figure 26.1. Privacy and security issues involving a biometric system.
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to the system, will produce a match. The artefact may not even resemble the original image. As was shown by Adler [5], a masquerade image can be reconstructed from a face template using a Hill Climbing attack (this attack can be applied to any biometrics). In more recent publications by Ross et al. [6] and Cappelli et al. [7], a masquerade image reconstructed from a minutiae template can actually resemble the original ngerprint (see also Mohanty et al. [8] for face recognition). The masquerade attack poses a real threat to remote authentication systems, since an attacker does not even need to acquire a genuine biometric sample. All he needs to do is gain access to the templates stored on a remote server. r FAR Attack on a Database: If an attacker can obtain access to all the templates stored in a database, he can run of ine his own biometric against the database in the hopes of obtaining a false acceptance with at least one of the stored templates. If, for example, the system false acceptance rate (FAR) is 0.01%, which is common for one-to-one veri cation, and the database contains more than 10,000 templates, the attacker has a good chance of nding a matching template. The attacker s biometric will serve as a masquerade image for this template. r Trojan Horse Attacks: Some parts of the system (e.g., a matcher), can be replaced by a Trojan horse program that always outputs high veri cation scores. r Overriding Yes/No Responses: An inherent aw of existing biometric systems is that the output of the system is always a binary Yes/No (i.e., match/no match) response. This makes the system open to potential attacks. For example, if an attacker were able to interject a false Yes response at a proper point of the
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