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Title: On using fuzzy data in security mechanisms
Author: Hao, F.
Awarding Body: University of Cambridge
Current Institution: University of Cambridge
Date of Award: 2007
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Biometric measurements create a strong binding between a person and his unique features, which may conflict with personal privacy. In this dissertation, we study these problems in detail and propose solutions. First, we propose a scheme to derive error-free keys from fuzzy data, such as iris codes. There are two types of errors within iris codes: background-noise errors and burst errors. Accordingly, we devise a two-layer error correction technique, which first corrects the background-noise errors using a Hadamard code, then the burst errors using a Reed-Solomon code. Based on a database of 700 iris images, we demonstrate that an error-free key of 140 bits can be reliably reproduced from genuine iris codes with a 99.5% success rate. In addition, despite the irrevocability of biometric data, the keys produced using our technique can be easily revoked or updated. Second, we address the search problem for a large fuzzy database that stores iris codes or data with a similar structure. Currently, the algorithm used in all public deployments of iris recognition is to search exhaustively through a database of iris codes, looking for a match that is close enough. We propose a much more efficient search algorithm: Beacon Guided Search (BGS). BGS works by indexing iris codes, adopting a “multiple colliding segments principle” and an early termination strategy to reduce the search range dramatically. We evaluate this algorithm using 632,500 real-world iris codes, showing a substantial speed-up over exhaustive search with negligible loss of precision. In addition, we demonstrate that our empirical findings match theoretical analysis. Finally, we study the veto problem in a biometrically-enabled threshold control scheme. In such a scheme, the access, say to a nuclear device, is controlled by several delegates. Each delegate’s biometrics act as one key, and the access is only granted when all keys are correctly supplied. We propose an Anonymous Veto Network (AV-net), which assigns each delegate the power to veto the biometric enrolments anonymously. Compared with past work, the AV-net construction provides the strongest protection of each delegate’s privacy against collusion.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available