4,080 research outputs found

    Fuzzy Authentication using Rank Distance

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    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

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    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

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    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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