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Entropy Extraction from Bio-Signals in Healthcare IoT

Published: 14 April 2015 Publication History

Abstract

In this paper, the theoretical approach to estimate the amount of entropy which can be extracted from heart-rate based biomedical signals has been considered. Mathematical models for estimating the values of min-entropy, Shannon entropy, and collision entropy have been created. This allows obtaining the theoretical background and estimations for upper bound of entropy that can be extracted from the biomedical inter-pulse interval signal for the usage in healthcare and biomedical applications. These results will be useful when estimating the security of healthcare systems and during the certification of the devices.

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cover image ACM Conferences
IoTPTS '15: Proceedings of the 1st ACM Workshop on IoT Privacy, Trust, and Security
April 2015
50 pages
ISBN:9781450334495
DOI:10.1145/2732209
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 14 April 2015

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Author Tags

  1. ecg
  2. entropy
  3. heart rate
  4. hrv
  5. inter-pulse interval
  6. mathematical model
  7. ppg

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ASIA CCS '15
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ASIA CCS '15: 10th ACM Symposium on Information, Computer and Communications Security
April 14 - March 14, 2015
Singapore, Republic of Singapore

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IoTPTS '15 Paper Acceptance Rate 5 of 13 submissions, 38%;
Overall Acceptance Rate 16 of 39 submissions, 41%

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  • (2023)Morphological features of the photoplethysmographic signal: a new approach to characterize the microcirculatory response to photobiomodulationFrontiers in Physiology10.3389/fphys.2023.117547014Online publication date: 25-Sep-2023
  • (2022)Breaking Barriers in Emerging Biomedical ApplicationsEntropy10.3390/e2402022624:2(226)Online publication date: 31-Jan-2022
  • (2021)IoT terminal security assessment system based on improved assessment methodPLOS ONE10.1371/journal.pone.025688116:9(e0256881)Online publication date: 8-Sep-2021
  • (2021)Internet of Things in Health Care: A SurveyHybrid Artificial Intelligence and IoT in Healthcare10.1007/978-981-16-2972-3_9(165-200)Online publication date: 23-Jul-2021
  • (2020)A Survey of Security Vulnerability Analysis, Discovery, Detection, and Mitigation on IoT DevicesFuture Internet10.3390/fi1202002712:2(27)Online publication date: 6-Feb-2020
  • (2020)An IoT Honeynet Based on Multiport Honeypots for Capturing IoT AttacksIEEE Internet of Things Journal10.1109/JIOT.2019.29561737:5(3991-3999)Online publication date: May-2020
  • (2019)On the Difficulty of Using Patient's Physiological Signals in Cryptographic ProtocolsProceedings of the 24th ACM Symposium on Access Control Models and Technologies10.1145/3322431.3325099(113-122)Online publication date: 28-May-2019
  • (2019)Overview of IoT Security Architecture2019 IEEE Fourth International Conference on Data Science in Cyberspace (DSC)10.1109/DSC.2019.00058(338-345)Online publication date: Jun-2019
  • (2018)Heartbeats Do Not Make Good Pseudo-Random Number Generators: An Analysis of the Randomness of Inter-Pulse IntervalsEntropy10.3390/e2002009420:2(94)Online publication date: 30-Jan-2018
  • (2018)FinDroidHRProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/31917882:1(1-42)Online publication date: 26-Mar-2018
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