Secure Fingerprint Alignment and Matching Protocols
This work addresses privacy concerns in biometric authentication for parties needing secure fingerprint comparison, though it is incremental by applying existing secure computation techniques to a new domain.
The paper tackles the problem of securely comparing privately-held fingerprints without revealing the underlying data, presenting three protocols based on minutia points that allow parties to learn only an accurate matching score. It introduces the first secure fingerprint alignment protocols using garbled circuits and secret sharing, achieving precise and efficient matching while designing secure sub-protocols for complex operations.
We present three private fingerprint alignment and matching protocols, based on what are considered to be the most precise and efficient fingerprint recognition algorithms, which use minutia points. Our protocols allow two or more honest-but-curious parties to compare their respective privately-held fingerprints in a secure way such that they each learn nothing more than an accurate score of how well the fingerprints match. To the best of our knowledge, this is the first time fingerprint alignment based on minutiae is considered in a secure computation framework. We build secure fingerprint alignment and matching protocols in both the two-party setting using garbled circuit evaluation and in the multi-party setting using secret sharing techniques. In addition to providing precise and efficient secure fingerprint alignment and matching, our contributions include the design of a number of secure sub-protocols for complex operations such as sine, cosine, arctangent, square root, and selection, which are likely to be of independent interest.