journal article·2022Open access

Chaos-Based Cryptographic Mechanism for Smart Healthcare IoT Systems

Taher M. Ghazal, Sagheer Abbas, Sundus Munir, M. A. Khan, Munir Ahmad, Ghassan F. Issa, Syeda Binish Zahra, Muhammad Adnan Khan, Mohammad Kamrul Hasan

Computers, Materials & Continua· Volume 71· Issue 1

Abstract

Smart and interconnected devices can generate meaningful patient data and exchange it automatically without any human intervention in order to realize the Internet of Things (IoT) in healthcare (HIoT). Due to more and more online security and data hijacking attacks, the confidentiality, integrity and availability of data are considered serious issues in HIoT applications. In this regard, lightweight block ciphers (LBCs) are promising in resource-constrained environment where security is the primary consideration. The prevalent challenge while designing an LBC for the HIoT environment is how to ascertain platform performance, cost, and security. Most of the existing LBCs primarily focus on text data or grayscale images. The main focus of this paper is about securing color images in a cost-effective way. We emphasis high confidentiality of color images captured by cameras in resource-constrained smartphones, and high confidentiality of sensitive images transmitted by low-power sensors in IoT systems. In order to reduce computational complexity and simulation time, the proposed Lightweight Symmetric Block Cipher (LSBC) exploits chaos-based confusion-diffusion operations at the inter-block level using a single round. The strength of LSBC is assessed by cryptanalysis, while it is ranked by comparing it to other privacy-preserving schemes. Our results show that the proposed cipher produces promising results in terms of key sensitivity and differential attacks, which proves that our LSBC is a good candidate for image security in HIoT.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Chaos-based Image/Signal Encryption · Cryptographic Implementations and Security · Fractal and DNA sequence analysis

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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