Discriminative binary feature learning and quantization in biometric key generation

Anees, Amir; Chen, Yi‐Ping Phoebe

doi:10.1016/j.patcog.2017.11.018

Cited by 41 publications

(22 citation statements)

References 38 publications

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“… 38 The basic topology and computation plans of FL via the aggregation server and peer‐to‐peer approaches are presented in Figure 3A-F, respectively. Although FL mainly serves for privacy preservation, where aggregation server approaches ensure participants remain unknown from each other, models subject to conditions retain some information 39 . To overcome privacy leakage in the FL framework, differential privacy 40,41 or encrypted data learning approaches 42 have been suggested.…”

Section: Fl Systemmentioning

confidence: 99%

A comprehensive review of federated learning for COVID‐19 detection

Naz

Phan

Chen

2021

Int J of Intelligent Sys

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The coronavirus of 2019 (COVID‐19) was declared a global pandemic by World Health Organization in March 2020. Effective testing is crucial to slow the spread of the pandemic. Artificial intelligence and machine learning techniques can help COVID‐19 detection using various clinical symptom data. While deep learning (DL) approach requiring centralized data is susceptible to a high risk of data privacy breaches, federated learning (FL) approach resting on decentralized data can preserve data privacy, a critical factor in the health domain. This paper reviews recent advances in applying DL and FL techniques for COVID‐19 detection with a focus on the latter. A model FL implementation use case in health systems with a COVID‐19 detection using chest X‐ray image data sets is studied. We have also reviewed applications of previously published FL experiments for COVID‐19 research to demonstrate the applicability of FL in tackling health research issues. Last, several challenges in FL implementation in the healthcare domain are discussed in terms of potential future work.

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Section: Fl Systemmentioning

confidence: 99%

A comprehensive review of federated learning for COVID‐19 detection

Naz

Phan

Chen

2021

Int J of Intelligent Sys

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“…Specifically, they adopted a transformation method based on shuffling to generate the revocable bio-key. Anees et al [40] presented a bio-key generation method based on binary feature extraction and quantization. However, these methods do not consider the intra-user variations, which makes it difficult to generate stable bio-keys.…”

Section: Key Generation Scheme Based On Biometricsmentioning

confidence: 99%

A Secure Biometric Key Generation Mechanism via Deep Learning and Its Application

Wang

Zhang

et al. 2021

Applied Sciences

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Biometric keys are widely used in the digital identity system due to the inherent uniqueness of biometrics. However, existing biometric key generation methods may expose biometric data, which will cause users’ biometric traits to be permanently unavailable in the secure authentication system. To enhance its security and privacy, we propose a secure biometric key generation method based on deep learning in this paper. Firstly, to prevent the information leakage of biometric data, we utilize random binary codes to represent biometric data and adopt a deep learning model to establish the relationship between biometric data and random binary code for each user. Secondly, to protect the privacy and guarantee the revocability of the biometric key, we add a random permutation operation to shuffle the elements of binary code and update a new biometric key. Thirdly, to further enhance the reliability and security of the biometric key, we construct a fuzzy commitment module to generate the helper data without revealing any biometric information during enrollment. Three benchmark datasets including ORL, Extended YaleB, and CMU-PIE are used for evaluation. The experiment results show our scheme achieves a genuine accept rate (GAR) higher than the state-of-the-art methods at a 1% false accept rate (FAR), and meanwhile satisfies the properties of revocability and randomness of biometric keys. The security analyses show that our model can effectively resist information leakage, cross-matching, and other attacks. Moreover, the proposed model is applied to a data encryption scenario in our local computer, which takes less than 0.5 s to complete the whole encryption and decryption at different key lengths.

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“…Other than these two major lines of research, the use of cryptographic tools has also been proposed for privacy-preserving machine learning [19,33,34,35,36,37]. Several differential privacy-based solutions have been proposed to prevent the GAN attack, in which a noise signal is added to gradients during the learning phase in order to achieve differential privacy, and hence prevent the GAN attack [38,39].…”

Section: Related Workmentioning

confidence: 99%

Key protected classification for collaborative learning

Sarıyıldız

Cinbiş

Ayday

2020

Pattern Recognition

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Large-scale datasets play a fundamental role in training deep learning models. However, dataset collection is difficult in domains that involve sensitive information. Collaborative learning techniques provide a privacy-preserving solution, by enabling training over a number of private datasets that are not shared by their owners. However, recently, it has been shown that the existing collaborative learning frameworks are vulnerable to an active adversary that runs a generative adversarial network (GAN) attack. In this work, we propose a novel classification model that is resilient against such attacks by design. More specifically, we introduce a key-based classification model and a principled training scheme that protects class scores by using class-specific private keys, which effectively hides the information necessary for a GAN attack. We additionally show how to utilize high dimensional keys to improve the robustness against attacks without increasing the model complexity. Our detailed experiments demonstrate the effectiveness of the proposed technique.

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Discriminative binary feature learning and quantization in biometric key generation

Cited by 41 publications

References 38 publications

A comprehensive review of federated learning for COVID‐19 detection

A comprehensive review of federated learning for COVID‐19 detection

A Secure Biometric Key Generation Mechanism via Deep Learning and Its Application

Key protected classification for collaborative learning

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