4,080 research outputs found
Fuzzy Authentication using Rank Distance
Fuzzy authentication allows authentication based on the fuzzy matching of two
objects, for example based on the similarity of two strings in the Hamming
metric, or on the similiarity of two sets in the set difference metric. Aim of
this paper is to show other models and algorithms of secure fuzzy
authentication, which can be performed using the rank metric. A few schemes are
presented which can then be applied in different scenarios and applications.Comment: to appear in Cryptography and Physical Layer Security, Lecture Notes
in Electrical Engineering, Springe
Multicriteria optimization to select images as passwords in recognition based graphical authentication systems
Usability and guessability are two conflicting criteria in assessing the
suitability of an image to be used as password in the recognition based graph -ical authentication systems (RGBSs). We present the first work in this area that
uses a new approach, which effectively integrates a series of techniques in order
to rank images taking into account the values obtained for each of the dimen -sions of usability and guessability, from two user studies. Our approach uses
fuzzy numbers to deal with non commensurable criteria and compares two
multicriteria optimization methods namely, TOPSIS and VIKOR. The results
suggest that VIKOR method is the most applicable to make an objective state-ment about which image type is better suited to be used as password. The paper
also discusses some improvements that could be done to improve the ranking
assessment
Privacy-Preserving Facial Recognition Using Biometric-Capsules
Indiana University-Purdue University Indianapolis (IUPUI)In recent years, developers have used the proliferation of biometric sensors in smart devices, along with recent advances in deep learning, to implement an array of biometrics-based recognition systems. Though these systems demonstrate remarkable performance and have seen wide acceptance, they present unique and pressing security and privacy concerns. One proposed method which addresses these concerns is the elegant, fusion-based Biometric-Capsule (BC) scheme. The BC scheme is provably secure, privacy-preserving, cancellable and interoperable in its secure feature fusion design.
In this work, we demonstrate that the BC scheme is uniquely fit to secure state-of-the-art facial verification, authentication and identification systems. We compare the performance of unsecured, underlying biometrics systems to the performance of the BC-embedded systems in order to directly demonstrate the minimal effects of the privacy-preserving BC scheme on underlying system performance. Notably, we demonstrate that, when seamlessly embedded into a state-of-the-art FaceNet and ArcFace verification systems which achieve accuracies of 97.18% and 99.75% on the benchmark LFW dataset, the BC-embedded systems are able to achieve accuracies of 95.13% and 99.13% respectively. Furthermore, we also demonstrate that the BC scheme outperforms or performs as well as several other proposed secure biometric methods
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