Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

OCTOPUS: A Novel Approach for Health Data Masking and Retrieving Using Physically Unclonable Function and Machine Learning

Version 1 : Received: 16 February 2023 / Approved: 17 February 2023 / Online: 17 February 2023 (08:40:29 CET)

How to cite: Satra, S.; Sadhu, P.; Yanambaka, V.P.; Abdelgawad, A. OCTOPUS: A Novel Approach for Health Data Masking and Retrieving Using Physically Unclonable Function and Machine Learning. Preprints 2023, 2023020306. https://doi.org/10.20944/preprints202302.0306.v1 Satra, S.; Sadhu, P.; Yanambaka, V.P.; Abdelgawad, A. OCTOPUS: A Novel Approach for Health Data Masking and Retrieving Using Physically Unclonable Function and Machine Learning. Preprints 2023, 2023020306. https://doi.org/10.20944/preprints202302.0306.v1

Abstract

The health equipment are used to keep track of significant health indicators, automate health interventions, and analyze health indicators. People have begun using mobile applications to track health characteristics and medical demands because all devices are linked to high-speed internet and phones. Such a combination of smart devices, the internet, and mobile applications expands the usage of remote health monitoring through the Internet of Medical Things (IoMT). The accessibility and unpredictable aspects of IoMT create massive security and confidentiality threats in IoMT systems. In this proposed paper - Octopus, Physically Unclonable Functions (PUFs) have been used to provide privacy to the healthcare device by masking the data, and machine learning (ML) technique is used to retrieve the health data back and for reducing security breaches on networks. This technique has exhibited 99.45% accuracy, which proves that this technique could be used to secure health data with masking.

Keywords

Internet of Medical Things; physical unclonable functions; machine learning; security and privacy

Subject

Engineering, Electrical and Electronic Engineering

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