In a wireless body area network (WBAN), group pairing among multiple wearable devices enables efficient and secure broadcasting group messages. Existing pairing methods that rely on trusted concentrators, active participation of users, or homogeneous environments are vulnerable to single point of failure and have restricted practicality. In this article, we propose Electrocardiogram-to-Pair (E2P), an electrocardiogram (ECG)-based secure group pairing protocol for WBAN devices that allows the establishment of a shared group key among multiple devices without requiring the user involvement or the central device. First, all wearable devices worn by the same user simultaneously collect ECG signals, which are then pre-processed through filtering and alignment to minimize the effect of noise on the pairing. Next, an adaptive quantization method is designed to reliably quantize the pre-processed ECG signals to generate initial keys. This method is ingenious as it relies on feature transformation and dynamic thresholds generation in the quantization process, which effectively ensures the security of biometrics and increases the key generation rate. Then, the group key is established among devices with initial keys through an improved Cascade method. The simulation results demonstrate that E2P achieves an effective balance the reliability and efficiency in group pairing, with outstanding performance in both security and key generation rate.

The increasing use of multi-biometric authentication has raised concerns about the security of biometric templates. Many template protection methods based on convolutional neural network have been presented, but most involve a trade-off between authentication accuracy and template security. In this paper, we present a cancelable multi-biometric template protection scheme that combines deep hashing with cancelable distance-preserving encryption (CDPE), which provides high template security without degrading the authentication performance. Specifically, a deep hashing based architecture that minimizes the quantization loss is designed to map face and iris traits to binary codes. Next, CDPE is proposed to generate a protected template given the face binary code and a user-specific key obtained from the iris binary code, which preserves the distance between original templates in the protected domain to ensure authentication performance equivalent to unprotected systems. Digital lockers instead of the key are stored to further enhance the security, which can be unlocked with genuine biometric traits to get the correct key during authentication. Theoretical and experimental results on real face and iris datasets show that our scheme can achieve equal error rate of 0.23% and genuine accept rate of 97.54%, while guaranteeing irreversibility, revocability and unlinkability of protected templates.

Face and voice are two of the most popular traits used for authentication tasks in daily life, as they can be easily captured using low-cost visual and audio sensors on smartphones, laptops, tablets,etc. Many bimodal biometric authentication schemes based on these two traits have been presented to provide higher accuracy than unimodal systems. However, these schemes are inflexibility due to the requirement of submitting two traits simultaneously, and they lack template protection, which may lead to biometric data leakage. We present a cross-modal learning based bimodal biometric authentication scheme, which improves the flexibility of existing schemes while ensuring the biometric template security. We integrate cross-modal learning into the feature extraction to obtain a bimodal biometric shared representation given input face images and voice clips. In order to enhance biometric template security without sacrificing authentication accuracy, a residual network and polar codes based template protection method is proposed, which can eliminate the noise in shared representations due to intra-user variations and generate protected templates. We have evaluated the efficacy of the bimodal biometric scheme using a real video dataset containing face images and voice clips. Experimental results demonstrate that our scheme can achieve flexible authentication with high accuracy no matter the probe input is a face image, a voice clip or a combination of them. Furthermore, the security analysis demonstrates that our scheme provides irreversibility, unlinkability and revocability of protected templates.

The widespread usage of wearables to provide healthcare services prompts the need for secure group communication among multiple devices using group keys. Gait-based group key establishment schemes are either vulnerable to video attacks, or fail to offer a secure group key update mechanism when group device changes. In this paper, we present an electrocardiogram (ECG) signals based group device pairing protocol, which can strengthen the security and reduce the overhead of wearables. Specifically, we first design a robust and lightweight fuzzy extractor that supports secure and efficient group device association between wearables. Meanwhile, we propose Improved Martingale Randomness Extraction (IMRE) algorithm, which utilizes the trend of InterPulse Interval (IPI) from ECG signal to extract high-entropy keys. Then we present a membership management mechanism that enables group key dynamic update when group device changes. Finally, we simulate our protocol and evaluate the accuracy and efficiency by various experiments. The experimental results demonstrate that the proposed work is robust and efficient, and the threat model-based security analysis shows that the proposed protocol can prevent both active and passive attacks.