Achieving secure communication between an Implantable Medical Device (IMD) inside the body and a gateway outside the body has showed its criticality with recent reports of hackings. The use of asymmetric cryptography is not a practical solution for IMDs due to the scarce computational and power resources, symmetric key cryptography is preferred. One of the factors in security of a symmetric cryptographic system is to use a strong key for encryption. A solution without using extensive resources in an IMD, is to extract it from the body physiological signals. To have a strong enough key, the physiological signal must be a strong source of randomness and InterPulse Interval (IPI) has been advised to be such that. A strong randomness source should have five conditions: Universality, Liveness, Robustness Permanence and Uniqueness. Nevertheless, for current proposed random extraction methods from IPI these conditions (mainly last three conditions) were not examined. In this study, firstly, we proposed a methodology to measure the last three conditions. Then, using a huge dataset of IPI values, we showed that IPI does not have conditions of Robustness and Permanence. Thus, extraction of a strong uniform random number from IPI value, mathematically, is impossible. Thirdly, rather than using the value of IPI, we proposed the trend of IPI as a source for a new randomness extraction method named as Martingale Randomness Extraction from IPI (MRE-IPI). MRE-IPI satisfies the Robustness condition completely and Permanence to some level. We, also, used randomness test suites and showed that MRE-IPI is able to outperform all recent randomness extraction methods from IPIs and its quality is half of the AES random number. To the best of our knowledge, this is the first work in this area which uses such a comprehensive method and large dataset to examine the randomness of a physiological signal.
A recent trend in the field of biometrics is ECG-based, where electrocardiogram (ECG) signals are used as input to the biometric system. Previous work has shown that ECG has a good potential, which can be used alone as a biometric parameter or in combination with some other parameters for greater accuracy, due to its main key properties. This paper presents a study on the applicability of ECG signals in securing Wireless Body Area Network (WBAN) communications. We study the permanence and the distinctiveness properties of ECG signals on 10 random patients. The Independent Component Analysis (ICA) and Fast Fourier Transform (FFT) are applied on the ECG signals obtained from MIT-BIH Normal Synus Rhythm (nsrdb) public database. The experimental results are presented, which exhibit that ECG signal can be utilized properly to achieve better security performance under the stringent constraints of WBAN sensors. Thus, it is believed that the system can naturally secure the information transmission within WBAN, where other techniques use hardware and software to achieve the same purpose. In this paper, we propose an innovative low-cost biometric authentication scheme based on uniformed Gaussian mixture model (GMM) for secure communications within wireless body area sensor networks (WBASNs), to obviate the cost-prohibitive key exchange problem. In this approach, the sender Inter Pulse Interval (IPI) information is selected as the biometric key for authentication, and a statistical based signing scheme is developed using the uniformed GMM. Experimental analysis shows that the proposed approach effectively protects the communication channels in WBASN with a low computational complexity, significantly removing the need for secret key exchange and critical time synchronization overheads.  The development of the wireless body area sensor network (BASN) is imperative for modern telemedicine and m-health, but security remains a formidable challenge yet to be resolved. As nodes of BASN are expected to be interconnected on or in the human body, the body itself can form an inherently secure communication pathway that is unavailable to all other kinds of wireless networks. This article explores the use of this conduit in the security mechanism of BASN; that is, by a biometrics approach that uses an intrinsic characteristic of the human body as the authentication identity or the means of securing the distribution of a cipher key to secure inter-BASN communications. The method was tested on 99 subjects with 838 segments of simultaneous recordings of electrocardiogram and photoplethysmogram. By using the interpulse interval (IPI) as the biometric trait, the system achieved a minimum half total error rate of 2.58 percent when the IPIs measured from signals, which were sampled at 1000 Hz, were coded into 128-bit binary sequences. The study opens up a few key issues for future investigation, including compensation schemes for the asynchrony of different channels, coding schemes, and other suitable biometric traits. Body Area Networks (BANs) are the most important building stone of pervasive healthcare, which enables remote, continuous and real-time health monitoring. Biosensors, constituting the BANs, collect highly sensitive medical information from their hosts and communicate these data. Considering the nature of the wireless medium, the privacy requirements of the individuals and the extreme energy and storage limitations of the biosensors, BANs require a light-weight and secure key management infrastructure. It has been suggested that the security of a BAN can be guaranteed using the body itself as the communication channel by means of bio-cryptography. Explicitly, physiological parameters generated from different body parts are used to protect the data exchanged among the biosensors. In this paper, we (i) define a novel physiological parameter generation technique, and (ii) identify and evaluate an appropriate physiological parameter that can be used in a biocryptographic key management protocol, namely the inter-pulse interval (IPI). For experimental data analysis, we use the blood pressure (BP) signal, for the first time in the literature, together with the electrocardiogram (ECG) and photoplethysmogram (PPG) signals. Our results show that the IPI values derived from the ECG, PPG and BP signals are good candidates of physiological parameters that can be used as cryptographic keys in order to ensure secure key management in BANs.  Securing body sensor network (BSN) in an efficient manner is very important for preserving the privacy of medical data. Protecting data confidentiality, integrity and to authenticate the communicating nodes are basic requirements to secure BSN. The existing method to generate entity identifier (EI) from inter-pulse intervals (IPIs) of heartbeats has its advantages in authenticating and identifying nodes, which however was found in this study that such generated EIs are not so resistant to attacks because of potential error patterns. This paper presents an improved scheme of IPI-based EI generation to eliminate the error patterns. The performance of randomness and node identification, i.e. false acceptance rate and false rejection rate, is experimentally evaluated. The results indicate that compared with the existing one, the new scheme is effective to eliminate the error patterns and thus more tolerant to attacks, while there is no compromise on the randomness level and identification performance. To address the critical problem of symmetric key distribution in the emerging wireless body sensor network, a family of novel biometrics methods has been recently proposed to achieve automatic key distribution. As the core of such biometrics methods, the quality of entity identifiers generated from bio-signals determines the maximal performance that can be achieved in such systems. This work evaluates the matching performance of inter-pulse interval based entity identifier, which is the most efficient one among the existing proposals, using the experimental data collected from 85 clinical subjects. The results of statistical analysis show that the error distribution of entity identifiers from genuine communicating pairs has a certain pattern, which should be taken into consideration while designing the consequent fuzzy identification scheme.  Recently, a kind of lightweight and resource-efficient biometrics-based security solutions were proposed for the emerging body sensor network (BSN). In such security solutions, physiological characteristics that can be captured by individual sensors of BSN were proposed to generate entity identifiers (EIs) for securing keying materials by a biometric approach. In this study, the authors focus on an improved key distribution solution with the energy distribution information of physiological signals (EDPSs) -based EIs. Firstly, different EDPS-based EI generation schemes are studied. Based on the existing multi-windows Fourier transform scheme, a modified one with single-window is proposed to improve the identification performance of the generated EIs. Then, a different method based on the discrete cosine transform of the autocorrelation sequence of physiological signals is proposed aiming for a significant increase in identification rates. The performances of time-varying randomness and identification rates are evaluated to examine EI's feasibility in securing the transmission of keying materials. Based on the characteristics of generated EIs, the corresponding key distribution solution, that is, user-dependent fuzzy vault, is proposed. A detailed system performance analysis in terms of half total error rate, anti-attack ability, as well as computational complexity, is conducted to demonstrate the effectiveness of the proposed solution.