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.
Whatever the extraction method is, it needs to be evaluated for the quality of generated randomness. Several extracting methods did not perform any randomness test to examine the quality of proposed algorithm (e.g. @cite_36 @cite_26 @cite_9 @cite_8 @cite_16 @cite_34 @cite_52 ). In these works the aim of the paper was not examining the randomness of proposed extraction method. There are other randomness extraction works in which there was an attempt to measure the quality of proposed method. However, in these works, two aspects have been somewhat ignored. To evaluate the quality of a randomness extractor, the dataset and the methodology of evaluation are the key points. For instance, @cite_39 evaluated the randomness property of their algorithm with 5 minutes ECG data of 10 subject by NIST Statistical Test Suite (STS) @cite_54 randomness test. @cite_50 @cite_31 @cite_13 tested their algorithm using 5 minutes ECG signal of 40 subjects with NIST STS. In another work, @cite_0 used histogram analysis on 1500 consecutive IPI values. @cite_21 tested their proposed method with 100 subjects' ECG data with Entropy test. @cite_1 , to evaluate the randomness of proposed algorithm, used Temporal Ratio @cite_58 method over 5 minutes ECG data of 50 subjects.
@cite_26 @cite_8 @cite_36 @cite_9 @cite_54 @cite_21 @cite_52 @cite_34 @cite_39 @cite_0 @cite_1 @cite_58 @cite_50 @cite_31 @cite_16 @cite_13
168352792 1517403092 1982949165 2049509357 2254371805 2006873322 2085831522 1565825153 2045462474
Key establishment is a fundamental service for secure communications between a central device and each biosensor implanted in the human body. It provides and manages the cryptographic keys to enable security services such as confidentiality, integrity and authentication. There exist several schemes to provide secure communications; however, some existing schemes employ unsuitable cryptography mechanisms or share a pre-deployed key in body sensor networks(BSNs). Due to stringent constraints of power, memory and computation capability, it is inadequate to apply asymmetric cryptography mechanisms to biosensors. Besides, it is also inappropriate to store the pre-deployed key in the implanted biosensor because, if the key is exposed, a person will have transplantation surgery for the secret to be redistributed. In this paper, we proposed the secure communication scheme only using symmetric cryptosystem without a pre-deployed key between a central device and each biosensor in BSNs. Abstract : This paper discusses some aspects of selecting and testing random and pseudorandom number generators. The outputs of such generators may he used in many cryptographic applications, such as the generation of key material. Generators suitable for use in cryptographic applications may need to meet stronger requirements than for other applications. In particular, their outputs must he unpredictable in the absence of knowledge of the inputs. Some criteria for characterizing and selecting appropriate generators are discussed in this document. The subject of statistical testing and its relation to cryptanalysis is also discussed, and some recommended statistical tests are provided. These tests may he useful as a first step in determining whether or not a generator is suitable for a particular cryptographic application. The design and cryptanalysis of generators is outside the scope of this paper. 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. Wireless Implantable Medical Devices (IMDs), including pacemakers and cardiac defibrillators, often have built-in wireless modules in order to facilitate non-invasive programming and data read-out; however, most IMDs lack a security mechanism. The unique challenge is that IMDs should be able to be accessed by doctors at any legitimate hospital for emergency purposes, but conventional security mechanisms using keys or credentials cannot guarantee that doctors could obtain keys timely in emergency situations. To address this unique challenge, we present an ECG-based Secret Data Sharing (ESDS) scheme for securing wireless IMDs which does not require pre-deployed keys or credentials. The ESDS scheme makes use of electrocardiograph (ECG) features to hide a secret within the IMD before transmission, and then reveal the secret with simultaneously measured ECG features by an external programmer. This scheme can protect IMDs against eavesdropping and other active attacks. In addition, the scheme uses an improved fuzzy vault scheme in which hash functions are substituted for chaff points to hide genuine points on a polynomial. This improvement saves IMD resources in terms of memory and communications, making the ESDS scheme lightweight. Performance analysis shows that the ESDS scheme meets our design goal of security. 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. The electrocardiogram (ECG also called EKG) trace expresses cardiac features that are unique to an individual. The ECG processing followed a logical series of experiments with quantifiable metrics. Data filters were designed based upon the observed noise sources. Fiducial points were identified on the filtered data and extracted digitally for each heartbeat. From the fiducial points, stable features were computed that characterize the uniqueness of an individual. The tests show that the extracted features are independent of sensor location, invariant to the individual's state of anxiety, and unique to an individual. 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.