Strong key derivation from noisy sources

• Main Author: Fuller, Benjamin Woodbury
• Language: en_US
• Published: 2016
• Subjects: Computer science; Biometric authentication; Error correcting codes; Fuzzy extractors; Information theory; Key derivation
• Online Access: https://hdl.handle.net/2144/15636

A shared cryptographic key enables strong authentication. Candidate sources for creating such a shared key include biometrics and physically unclonable functions. However, these sources come with a substantial problem: noise in repeated readings. A fuzzy extractor produces a stable key from a noisy source. It consists of two stages. At enrollment time, the generate algorithm produces a key from an initial reading of the source. At authentication time, the reproduce algorithm takes a repeated but noisy reading of the source, yielding the same key when the two readings are close. For many sources of practical importance, traditional fuzzy extractors provide no meaningful security guarantee. This dissertation improves key derivation from noisy sources. These improvements stem from three observations about traditional fuzzy extractors. First, the only property of a source that standard fuzzy extractors use is the entropy in the original reading. We observe that additional structural information about the source can facilitate key derivation. Second, most fuzzy extractors work by first recovering the initial reading from the noisy reading (known as a secure sketch). This approach imposes harsh limitations on the length of the derived key. We observe that it is possible to produce a consistent key without recovering the original reading of the source. Third, traditional fuzzy extractors provide information-theoretic security. However, security against computationally bounded adversaries is sufficient. We observe fuzzy extractors providing computational security can overcome limitations of traditional approaches. The above observations are supported by negative results and constructions. As an example, we combine all three observations to construct a fuzzy extractor achieving properties that have eluded prior approaches. The construction remains secure even when the initial enrollment phase is repeated multiple times with noisy readings. Furthermore, for many practical sources, reliability demands that the tolerated noise is larger than the entropy of the original reading. The construction provides security for sources of this type by utilizing additional source structure, producing a consistent key without recovering the original reading, and providing computational security.