Random Hash Code Generation for Cancelable Fingerprint Templates using Vector Permutation and Shift-order Process

• URL: http://arxiv.org/abs/2105.10227v1
• Date: Fri, 21 May 2021 09:37:54 GMT
• Title: Random Hash Code Generation for Cancelable Fingerprint Templates using Vector Permutation and Shift-order Process
• Authors: Sani M. Abdullahi and Sun Shuifa
• Abstract summary: We propose a non-invertible distance preserving scheme based on vector permutation and shift-order process. A shift-order process is then applied to the generated features in order to achieve non-invertibility and combat similarity-based attacks. The generated hash codes are resilient to different security and privacy attacks whilst fulfilling the major revocability and unlinkability requirements.
• Score: 3.172761915061083
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Cancelable biometric techniques have been used to prevent the compromise of biometric data by generating and using their corresponding cancelable templates for user authentication. However, the non-invertible distance preserving transformation methods employed in various schemes are often vulnerable to information leakage since matching is performed in the transformed domain. In this paper, we propose a non-invertible distance preserving scheme based on vector permutation and shift-order process. First, the dimension of feature vectors is reduced using kernelized principle component analysis (KPCA) prior to randomly permuting the extracted vector features. A shift-order process is then applied to the generated features in order to achieve non-invertibility and combat similarity-based attacks. The generated hash codes are resilient to different security and privacy attacks whilst fulfilling the major revocability and unlinkability requirements. Experimental evaluation conducted on 6 datasets of FVC2002 and FVC2004 reveals a high-performance accuracy of the proposed scheme better than other existing state-of-the-art schemes.

Related papers

• GEC-DePenD: Non-Autoregressive Grammatical Error Correction with Decoupled Permutation and Decoding [52.14832976759585]
  Grammatical error correction (GEC) is an important NLP task that is usually solved with autoregressive sequence-to-sequence models. We propose a novel non-autoregressive approach to GEC that decouples the architecture into a permutation network. We show that the resulting network improves over previously known non-autoregressive methods for GEC.
  arXiv  Detail & Related papers  (2023-11-14T14:24:36Z)
• AdjointDPM: Adjoint Sensitivity Method for Gradient Backpropagation of Diffusion Probabilistic Models [103.41269503488546]
  Existing customization methods require access to multiple reference examples to align pre-trained diffusion probabilistic models with user-provided concepts. This paper aims to address the challenge of DPM customization when the only available supervision is a differentiable metric defined on the generated contents. We propose a novel method AdjointDPM, which first generates new samples from diffusion models by solving the corresponding probability-flow ODEs. It then uses the adjoint sensitivity method to backpropagate the gradients of the loss to the models' parameters.
  arXiv  Detail & Related papers  (2023-07-20T09:06:21Z)
• OTB-morph: One-Time Biometrics via Morphing [16.23764869038004]
  This paper introduces a new idea to exploit as a transformation function for cancelable biometrics. An experimental implementation of the proposed scheme is given for face biometrics.
  arXiv  Detail & Related papers  (2023-02-17T18:39:40Z)
• Towards Privacy-Preserving, Real-Time and Lossless Feature Matching [8.418466369442413]
  This paper proposes a plug-in module called Secure that protects features by random permutations, 4L-DEC and existing homomorphic encryption techniques. For the first time, Secure achieves real-time and lossless feature matching among public features, along with much higher security levels than current state-of-the-arts.
  arXiv  Detail & Related papers  (2022-07-30T13:25:59Z)
• A Generalized Approach for Cancellable Template and Its Realization for Minutia Cylinder-Code [11.442790699169363]
  Index-of-Max (IoM) is a ranking-based locality sensitive hashing technique. biometric templates are not always in the form of ordered and fixed-length, rather it may be an unordered and variable size point set. We propose a generalized version of IoM hashing namely gIoM, and therefore the unordered and variable size biometric template can be used.
  arXiv  Detail & Related papers  (2022-03-02T13:30:46Z)
• Authentication Attacks on Projection-based Cancelable Biometric Schemes [0.6499759302108924]
  Cancelable biometric schemes aim at generating secure biometric templates by combining user specific tokens, such as password, stored secret or salt, along with biometric data. The security requirements of cancelable biometric schemes concern the irreversibility, unlinkability and revocability of templates, without losing in accuracy of comparison. In this paper, we formalize these attacks for a traditional cancelable scheme with the help of integer linear programming (ILP) and quadratically constrained quadratic programming (QCQP)
  arXiv  Detail & Related papers  (2021-10-28T14:39:35Z)
• Security and Privacy Enhanced Gait Authentication with Random Representation Learning and Digital Lockers [3.3549957463189095]
  Gait data captured by inertial sensors have demonstrated promising results on user authentication. Most existing approaches stored the enrolled gait pattern insecurely for matching with the pattern, thus, posed critical security and privacy issues. We present a gait cryptosystem that generates from gait data the random key for user authentication, meanwhile, secures the gait pattern.
  arXiv  Detail & Related papers  (2021-08-05T06:34:42Z)
• Orthogonalizing Convolutional Layers with the Cayley Transform [83.73855414030646]
  We propose and evaluate an alternative approach to parameterize convolutional layers that are constrained to be orthogonal. We show that our method indeed preserves orthogonality to a high degree even for large convolutions.
  arXiv  Detail & Related papers  (2021-04-14T23:54:55Z)
• Improved, Deterministic Smoothing for L1 Certified Robustness [119.86676998327864]
  We propose a non-additive and deterministic smoothing method, Deterministic Smoothing with Splitting Noise (DSSN) In contrast to uniform additive smoothing, the SSN certification does not require the random noise components used to be independent. This is the first work to provide deterministic "randomized smoothing" for a norm-based adversarial threat model.
  arXiv  Detail & Related papers  (2021-03-17T21:49:53Z)
• Sparse PCA via $l_{2,p}$-Norm Regularization for Unsupervised Feature Selection [138.97647716793333]
  We propose a simple and efficient unsupervised feature selection method, by combining reconstruction error with $l_2,p$-norm regularization. We present an efficient optimization algorithm to solve the proposed unsupervised model, and analyse the convergence and computational complexity of the algorithm theoretically.
  arXiv  Detail & Related papers  (2020-12-29T04:08:38Z)
• Certified Robustness to Label-Flipping Attacks via Randomized Smoothing [105.91827623768724]
  Machine learning algorithms are susceptible to data poisoning attacks. We present a unifying view of randomized smoothing over arbitrary functions. We propose a new strategy for building classifiers that are pointwise-certifiably robust to general data poisoning attacks.
  arXiv  Detail & Related papers  (2020-02-07T21:28:30Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.