Exploring a Brain-Based Cancelable Biometrics for Smart Headwear: Concept, Implementation, and Evaluation

Lin, Feng; Cho, Kun Woo; Song, Chen; Jin, Zhanpeng; Xu, Wenyao

doi:10.1109/tmc.2019.2936559

Cited by 9 publications

(7 citation statements)

References 61 publications

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“…Currently, most studies utilize EEG as the main source for biometric construction due to the convenience of signal acquisition [ 19 , 20 , 21 , 22 , 23 , 24 ]. However, a major problem with EEG is its low SNR because the brain’s electrical signals decay significantly while passing through the skull and scalp.…”

Section: Discussionmentioning

confidence: 99%

“…However, the keys generated by behavioral biometrics are generally very short, e.g., approximately 40 bits [ 16 ], which may be insufficient security for applications with high security requirements. Recent studies have demonstrated that the human brain can provide superior revocable biometric features [ 19 , 20 , 21 , 22 , 23 , 24 ]. Brain waves are continuous physiological signals with variable characteristics that change in real time; thus, it is extremely difficult to steal or imitate brain signals.…”

Section: Introductionmentioning

confidence: 99%

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Encrypt with Your Mind: Reliable and Revocable Brain Biometrics via Multidimensional Gaussian Fitted Bit Allocation

Li,

Qi,

Pan

2023

Bioengineering

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Biometric features, e.g., fingerprints, the iris, and the face, have been widely used to authenticate individuals. However, most biometrics are not cancellable, i.e., once these biometric features are cloned or stolen, they cannot be replaced easily. Unlike traditional biometrics, brain biometrics are extremely difficult to clone or forge due to the natural randomness across different individuals, which makes them an ideal option for identity authentication. Most existing brain biometrics are based on electroencephalogram (EEG), which is usually demonstrated unstable performance due to the low signal-to-noise ratio (SNR). For the first time, we propose the use of intracortical brain signals, which have higher resolution and SNR, to realize the construction of the high-performance brain biometrics. Specifically, we put forward a novel brain-based key generation approach called multidimensional Gaussian fitted bit allocation (MGFBA). The proposed MGFBA method extracts keys from the local field potential of ten rats with high reliability and high entropy. We found that with the proposed MGFBA, the average effective key length of the brain biometrics was 938 bits, while achieving high authentication accuracy of 88.1% at a false acceptance rate of 1.9%, which is significantly improved compared to conventional EEG-based approaches. In addition, the proposed MGFBA-based keys can be conveniently revoked using different motor behaviors with high entropy. Experimental results demonstrate the potential of using intracortical brain signals for reliable authentication and other security applications.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Encrypt with Your Mind: Reliable and Revocable Brain Biometrics via Multidimensional Gaussian Fitted Bit Allocation

Li,

Qi,

Pan

2023

Bioengineering

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“…Different approaches to biometric recognition using wearables can be found in the literature [ 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 ]. However, those based on gait can be considered one of the most important and popular due to its ease of acquisition: all current wearables have inertial sensors, which are the signals usually used for gait recognition [ 13 ].…”

Section: Related Workmentioning

confidence: 99%

“…With the popularization of these devices, the biometric community has shown considerable interest in them, due to their ability to capture both physiological and behavioral personal data, their use not being restricted to gait recognition. Some examples of these different approaches are heart-based authentication (using PhotoPlethysmoGraphy sensors [ 5 ] or Electrocardiogram [ 6 , 7 ]), speech [ 8 , 9 ], keystroke [ 10 ], galvanic skin response [ 11 ], and non-volitional brain response [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

A New Post-Processing Proposal for Improving Biometric Gait Recognition Using Wearable Devices

Salvador-Ortega

Vivaracho-Pascual

Simon-Hurtado

2023

Sensors

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In this work, a novel Window Score Fusion post-processing technique for biometric gait recognition is proposed and successfully tested. We show that the use of this technique allows recognition rates to be greatly improved, independently of the configuration for the previous stages of the system. For this, a strict biometric evaluation protocol has been followed, using a biometric database composed of data acquired from 38 subjects by means of a commercial smartwatch in two different sessions. A cross-session test (where training and testing data were acquired in different days) was performed. Following the state of the art, the proposal was tested with different configurations in the acquisition, pre-processing, feature extraction and classification stages, achieving improvements in all of the scenarios; improvements of 100% (0% error) were even reached in some cases. This shows the advantages of including the proposed technique, whatever the system.

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“…Unlike traditional identifiers such as username/password, once a biometric identifier is stolen, there is no way to change and replace it with a newer version. Addressing the challenge, some recent studies propose to use secure enclave equipment, cancellable biometric models, or pseudo-biometric identities [192]. However, such implementation may result in poor performance for objective reasons, such as aging or worn-out fingerprints.…”

Section: Remaining Challengesmentioning

confidence: 99%

Security and privacy for 6G: A survey on prospective technologies and challenges

Nguyen,

Lin,

Cheng

et al. 2021

Preprint

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Sixth-generation (6G) mobile networks will have to cope with diverse threats on a space-air-ground integrated network environment, novel technologies, and an accessible user information explosion. However, for now, security and privacy issues for 6G remain largely in concept. This survey provides a systematic overview of security and privacy issues based on prospective technologies for 6G in the physical, connection, and service layers, as well as through lessons learned from the failures of existing security architectures and state-of-the-art defenses.Two key lessons learned are as follows. First, other than inheriting vulnerabilities from the previous generations, 6G has new threat vectors from new radio technologies, such as the exposed location of radio stripes in ultra-massive MIMO systems at Terahertz bands and attacks against pervasive intelligence. Second, physical layer protection, deep network slicing, quantum-safe communications, artificial intelligence (AI) security, platform-agnostic security, real-time adaptive security, and novel data protection mechanisms such as distributed ledgers and differential privacy are the top promising techniques to mitigate the attack magnitude and personal data breaches substantially.

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Exploring a Brain-Based Cancelable Biometrics for Smart Headwear: Concept, Implementation, and Evaluation

Cited by 9 publications

References 61 publications

Encrypt with Your Mind: Reliable and Revocable Brain Biometrics via Multidimensional Gaussian Fitted Bit Allocation

Encrypt with Your Mind: Reliable and Revocable Brain Biometrics via Multidimensional Gaussian Fitted Bit Allocation

A New Post-Processing Proposal for Improving Biometric Gait Recognition Using Wearable Devices

Security and privacy for 6G: A survey on prospective technologies and challenges

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