Roles and capabilities of analysts are changing as the volume of data grows. Open-source content is abundant and users
are becoming increasingly dependent on automated capabilities to sift and correlate information. Entity resolution is one
such capability. It is an algorithm that links entities using an arbitrary number of criteria (e.g., identifiers, attributes)
from multiple sources. This paper demonstrates a prototype capability, which identifies enriched attributes of individuals
stored across multiple sources. Here, the system first completes its processing on a cloud-computing cluster. Then, in a
data explorer role, the analyst evaluates whether automated results are correct and whether attribute enrichment
improves knowledge discovery.
In order to fulfill the potential of fingerprint templates as the basis for authentication schemes, one needs to design a hash function for fingerprints that achieves acceptable matching accuracy and simultaneously has provable security guarantees, especially for parameter regimes that are needed to match fingerprints in practice. While existing matching algorithms can achieve impressive matching accuracy, they have no security guarantees. On the other hand, provable secure hash functions have bad matching accuracy and/or do not guarantee security when parameters are set to practical values. In this work, we present a secure hash function that has the best known tradeoff between security guarantees and matching accuracy. At a high level, our hash function is simple: we apply an off-the shelf hash function on certain collections of minutia points (in particular, triplets of minutia triangles"). However, to realize the potential of this scheme, we have to overcome certain theoretical and practical hurdles. In addition to the novel idea of combining clustering ideas from matching algorithms with ideas from the provable security of hash functions, we also apply an intermediate translation-invariant but rotation-variant map to the minutia points before applying the hash function. This latter idea helps improve the tradeoff between matching accuracy and matching efficiency.
In this work we place some of the traditional biometrics work on fingerprint verification via the fuzzy vault scheme within a cryptographic framework. We show that the breaking of a fuzzy vault leads to decoding of Reed-Solomon codes from random errors, which has been proposed as a hard problem in the cryptography community. We provide a security parameter for the fuzzy vault in terms of the decoding problem, which gives context for the breaking of the fuzzy vault, whereas most of the existing literature measures the strength of the fuzzy vault in terms of its resistance to pre-defined attacks or by the entropy of the vault. We keep track of our security parameter, and provide it alongside ROC statistics. We also aim to be more aware of the nature of the fingerprints when placing them in the fuzzy vault, noting that the distribution of minutiae is far from uniformly random. The results we show provide additional support that the fuzzy vault can be a viable scheme for secure fingerprint verification.