Pedro Peris‐Lopez

Caterina Fuster-Barceló, Carmen Cámara, Pedro Peris-López

Over the course of the past two decades, a substantial body of research has substantiated the viability of utilising cardiac signals as a biometric modality. This paper presents a novel approach for patient identification in healthcare systems using electrocardiogram signals. A convolutional neural network (CNN) is employed to classify users based on electrocardiomatrices, a specific type of image derived from ECG signals. The proposed identification system is evaluated in multiple databases, achieving up to 99.84\% accuracy on healthy subjects, 97.09\% on patients with cardiovascular diseases, and 97.89% on mixed populations including both healthy and arrhythmic patients. The system also performs robustly under varying activity conditions, achieving 91.32% accuracy in scenarios involving different physical activities. These consistent and reliable results, with low error rates such as a FAR of 0.01% and FRR of 0.157% in the best cases, demonstrate the method's significant advancement in subject identification within healthcare systems. By considering patients' cardiovascular conditions and activity levels, the proposed approach addresses gaps in the existing literature, positioning it as a strong candidate for practical applications in real-world healthcare settings.

Majid Salimi, Hamid Mala, Honorio Martin et al.

Multi-Party Non-Interactive Key Exchange (MP-NIKE) is a fundamental cryptographic primitive in which users register into a key generation centre and receive a public/private key pair each. After that, any subset of these users can compute a shared key without any interaction. Nowadays, IoT devices suffer from a high number and large size of messages exchanged in the Key Management Protocol (KMP). To overcome this, an MP-NIKE scheme can eliminate the airtime and latency of messages transferred between IoT devices. MP-NIKE schemes can be realized by using multilinear maps. There are several attempts for constructing multilinear maps based on indistinguishable obfuscation, lattices and the Chinese Remainder Theorem (CRT). Nevertheless, these schemes are inefficient in terms of computation cost and memory overhead. Besides, several attacks have been recently reported against CRT-based and lattice-based multilinear maps. There is only one modular exponentiation-based MP-NIKE scheme in the literature which has been claimed to be both secure and efficient. In this article, we present an attack on this scheme based on the Euclidean algorithm, in which two colluding users can obtain the shared key of any arbitrary subgroup of users. We also propose an efficient and secure MP-NIKE scheme. We show how our proposal is secure in the random oracle model assuming the hardness of the root extraction modulo a composite number.