Study on peak misdetection recovery of key exchange protocol using heartbeat

Kim, Ju‐Young; Cho, Kwantae; Kim, Yong-Kyun; Lim, Kyung-Soo; Shin, Sang Uk

doi:10.1007/s11227-018-2616-y

Cited by 9 publications

(16 citation statements)

References 16 publications

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“…Each IPI is transformed into a sequence of random bits, and we assume that legitimate sensors, only, can retrieve that sequence, by simultaneously measuring ECG from two different locations and applying the proposed key agreement protocol. Previous literature has already proposed IPI-based security mechanisms: they mainly employed fuzzy commitment and fuzzy vault [7]- [10]. On the contrary, we provide an analytical solution to authenticate two nodes within a WBAN: we can show that a relationship between information reconciliation and privacy amplification matrices can be found.…”

Section: Introductionmentioning

confidence: 88%

“…They found that roughly one minute is the expected time for securely exchanging an 80-bit key with high probability (1-10 −6 ), while consuming only 88 µJ from an IMD. Some improvements (both in terms of computational and communication cost and key establishment time) of the abovementioned scheme were reported in [8]- [10]. In particular, in [8], fuzzy-vault based protocol was shown to shorten the key establishment time by reducing the number of IPIs needed to generate a secret key.…”

Section: Related Workmentioning

confidence: 99%

“…The authors could dramatically reduce energy consumption (in communication and computation) compared to the state-of-the-art (e.g., [7]), while keeping secure key exchange. Finally, in [10] the authors proposed an improvement over the approach described in [7], by introducing a new method to detect mis-detections and recover IPIs using thresholds. They could show a significant reduction of the cost for communications (which was not only energy efficient, but it was able to generate secure keys).…”

Section: Related Workmentioning

confidence: 99%

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Information Theoretic Key Agreement Protocol based on ECG signals

Guglielmi¹,

Muraro²,

Cisotto³

et al. 2021

Preprint

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Wireless body area networks (WBANs) are becoming increasingly popular as they allow individuals to continuously monitor their vitals and physiological parameters remotely from the hospital. With the spread of the SARS-CoV-2 pandemic, the availability of portable pulse-oximeters and wearable heart rate detectors has boomed in the market. At the same time, in 2020 we assisted to an unprecedented increase of healthcare breaches, revealing the extreme vulnerability of the current generation of WBANs. Therefore, the development of new security protocols to ensure data protection, authentication, integrity and privacy within WBANs are highly needed. Here, we targeted a WBAN collecting ECG signals from different sensor nodes on the individual's body, we extracted the inter-pulse interval (i.e., R-R interval) sequence from each of them, and we developed a new information theoretic key agreement protocol that exploits the inherent randomness of ECG to ensure authentication between sensor pairs within the WBAN. After proper pre-processing, we provide an analytical solution that ensures robust authentication; we provide a unique information reconciliation matrix, which gives good performance for all ECG sensor pairs; and we can show that a relationship between information reconciliation and privacy amplification matrices can be found. Finally, we show the trade-off between the level of security, in terms of key generation rate, and the complexity of the error correction scheme implemented in the system.

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Section: Introductionmentioning

confidence: 88%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Information Theoretic Key Agreement Protocol based on ECG signals

Guglielmi¹,

Muraro²,

Cisotto³

et al. 2021

Preprint

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“…Another research is conducted to reduce the cost of communication by providing a method for detecting detection errors and recovering the inter-pulse interval using thresholds. There is a method that reduces the cost of communication compared to previous approaches, not only energy efficient but also key generating [18]. The efficiency of the recovery method is also demonstrated.…”

Section: Communication Networkmentioning

confidence: 99%

Machine learning on big data for future computing

2019

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IntroductionRecently, the integration of big data and machine learning technologies has emerged as an exciting new paradigm for industry and life. These technologies are fast-growing areas of research that cover the fields of Information and Communication Technologies (ICT) for future computing (FC). It becomes a preferable solution for solving complex problems in different fields, such as artificial intelligence, large-scale analysis systems, natural language processing, pattern recognition, video analysis, and telematics. For instance, AlphaGo, developed by Google DeepMind, changed the current paradigm of machine learning on big data after its steady win in the Google DeepMind Challenge. Machine learning is the art and science of designing computer algorithms from the dataset, and AlphaGo learns and improves performance by relying on massive data associated with huge games played by humans to improve artificial intelligence. Likewise, there are many issues to realize and provide effective services and applications with enormous attentions, and efforts have been focused on machine learning on big data for FC. Contributions to theoretical research presenting advanced technologies and concepts, analysis and reporting of experiences, and experiences related to the implementation and application of theories, as well as tutorials on emerging trends are required for these fields of research. As such, the purpose of this special issue is to provide an overview of the state-ofthe-art technologies and solution guidelines for these areas of research.This special issue covers pure research and applications within novel scopes related to machine learning on big data for future computing. In addition, it deals

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“…The key distribution problem between two devices (e.g., two ECG sensors [16]) has also attracted the attention of some researchers. In detail, in these solutions, each sensor derives the shared key from the acquired physiological signal, preventing the sensors from sharing any information beforehand [17,18]. In addition, the extraction of randomness from physiological signals has been recently scrutinised (e.g., ECG [19,20], EEG [21,22] and EMG [23]).…”

Section: Introductionmentioning

confidence: 99%

Design and Analysis of a True Random Number Generator Based on GSR Signals for Body Sensor Networks

Cámara

Martín

Peris-López

et al. 2019

Sensors

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Today, medical equipment or general-purpose devices such as smart-watches or smart-textiles can acquire a person’s vital signs. Regardless of the type of device and its purpose, they are all equipped with one or more sensors and often have wireless connectivity. Due to the transmission of sensitive data through the insecure radio channel and the need to ensure exclusive access to authorised entities, security mechanisms and cryptographic primitives must be incorporated onboard these devices. Random number generators are one such necessary cryptographic primitive. Motivated by this, we propose a True Random Number Generator (TRNG) that makes use of the GSR signal measured by a sensor on the body. After an exhaustive analysis of both the entropy source and the randomness of the output, we can conclude that the output generated by the proposed TRNG behaves as that produced by a random variable. Besides, and in comparison with the previous proposals, the performance offered is much higher than that of the earlier works.

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Study on peak misdetection recovery of key exchange protocol using heartbeat

Cited by 9 publications

References 16 publications

Information Theoretic Key Agreement Protocol based on ECG signals

Information Theoretic Key Agreement Protocol based on ECG signals

Machine learning on big data for future computing

Design and Analysis of a True Random Number Generator Based on GSR Signals for Body Sensor Networks

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