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.

Extracting Randomness From The Trend of IPI for Cryptographic Operators in Implantable Medical Devices

Achieving secure communication between an Implantable Medical Device (IMD) and a gateway or programming device outside the body has showed its criticality in recent reports of vulnerabilities in cardiac devices, insulin pumps and neural implants, amongst others. The use of asymmetric cryptography is typically not a practical solution for IMDs due to the scarce computational and power resources. Symmetric key cryptography is preferred but its security relies on agreeing and using strong keys, which are difficult to generate. A solution to generate strong shared keys without using extensive resources, is to extract them from physiological signals already present inside the body such as the Inter-Pulse interval (IPI). The physiological signals must therefore be strong sources of randomness that meet five conditions: Universality (available on all people), Liveness (available at any-time), Robustness (strong random number), Permanence (independent from its history) and Uniqueness (independent from other sources). However, these conditions (mainly the last three) have not been systematically examined in current methods for randomness extraction from IPI. In this study, we first propose a methodology to measure the last three conditions: Information secrecy measures for Robustness , Santha-Vazirani Source $delta$ d e l t a value for Permanence and random sources dependency analysis for Uniqueness . Then, using a large dataset of IPI values (almost 900,000,000 IPIs), we show that IPI does not have Robustness and Permanence as a randomness source. Thus, extraction of a strong uniform random number from IPI values is impossible. Third, we propose to use the trend of IPI, instead of its value, as a source for a new randomness extraction method named Martingale Randomness Extraction from IPI (MRE-IPI). We evaluate MRE-IPI and show that it satisfies the Robustness condition completely and Permanence to some level. Finally, we use the NIST STS and Dieharder test suites and show that MRE-IPI is able to outperform all recent randomness extraction methods from IPIs and achieves a quality roughly half that of the AES random number generator. MRE-IPI is still not a strong random number and cannot be used as key to secure communications in general. However, it can be used as a one-time pad to securely exchange keys between the communication parties. The usage of MRE-IPI will thus be kept at a minimum and reduces the probability of breaking it. 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 physiological signals.

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Extracting Randomness from the Trend of IPI for Cryptographic Operations in Implantable Medical Devices

Background: We aimed to extract the histogram features for text analysis and, to classify the types of Bio Medical Waste (BMW) for garbage disposal and management Methods: The given BMW was preprocessed by using the median filtering technique that efficiently reduced the noise in the image After that, the histogram features of the filtered image were extracted with the help of proposed Modified Local Tetra Pattern (MLTrP) technique Finally, the Relevance Vector Machine (RVM) was used to classify the BMW into human body parts, plastics, cotton and liquids Results: The BMW image was collected from the garbage image dataset for analysis The performance of the proposed BMW identification and classification system was evaluated in terms of sensitivity, specificity, classification rate and accuracy with the help of MATLAB When compared to the existing techniques, the proposed techniques provided the better results Conclusion: This work proposes a new texture analysis and classification technique for BMW management and disposal It can be used in many real time applications such as hospital and healthcare management systems for proper BMW disposal

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A Bio Medical Waste Identification and Classification Algorithm Using Mltrp and Rvm

A physiological signal must have a certain level of randomness inside it to be a good source of randomness for generating cryptographic key. Dependency to the history is one of the measures to examine the strength of a randomness source. In dependency to the history, the adversary has infinite access to the history of generated random bits from the source and wants to predict the next random number based on that. Although many physiological signals have been proposed in literature as good source of randomness, no dependency to history analysis has been carried out to examine this fact. In this paper, using a large dataset of physiological signals collected from PhysioNet, the dependency to history of Interpuls Interval (IPI), QRS Complex, and EEG signals (including Alpha, Beta, Delta, Gamma and Theta waves) were examined. The results showed that despite the general assumption that the physiological signals are random, all of them are weak sources of randomness with high dependency to their history. Among them, Alpha wave of EEG signal shows a much better randomness and is a good candidate for post-processing and randomness extraction algorithm.

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Randomness of physiological signals in generation cryptographic key for secure communication between implantable medical devices inside the body and the outside world

Medical sensor networks play a vital role for real-time health care monitoring of telemedicine based applications. Telemedicine provide specialized healthcare consultation to patients in remote locations. We use electronic information and communication technologies to provide and support healthcare when the distance separate the participants. In order to ensure the privacy and security of patient's critical health information, it is essential to provide efficient cryptography scheme. This paper presents a novel Mamdani based Bio-Key Management (MBKM) technique, which assures real time health care monitoring without any overhead. We present the simulation results to show that the proposed MBKM scheme can achieve greater security in terms of performance metrics such as False Match Rate (FMR), False Non Match Rate (FNMR), and Genuine Acceptance Rate (GAR) than other recent existing approaches.

V. Anjalipriya

Papers

An efficient Bio-key Management scheme for telemedicine applications