article · Expert Systems with Applications
Intelligent wearable devices in healthcare monitoring systems gather sensitive patient data that are vulnerable to privacy breaches. To protect these records, a dual compression and encryption framework has been developed for medical images. The framework uses an adaptive sparse basis compressive sensing model built on singular value decomposition, alongside a fractional-order Hopfield neural network incorporating a novel memristor to generate pseudo-random numbers with hyperchaotic behaviour. In this process, a medical image undergoes compression and bidirectional diffusion directed by key-controlled cipher streams, converting it into an encrypted image stripped of visual semantic features. Simulations indicate that this architecture resists diverse security attacks while maintaining a balance between data compressibility and system robustness.
Modern healthcare increasingly relies on wearable devices to monitor patient well-being, but transmitting unencrypted medical data creates substantial privacy risks. Integrating compression directly with encryption allows sensitive health images to be secured against cyberattacks without placing excessive storage or bandwidth burdens on wearable systems, safeguarding personal medical information during digital transmission.
This technology could be applied to secure medical data transmission in wearable healthcare monitoring systems and connected clinical devices. Potential users include digital health device manufacturers and healthcare monitoring platform developers. Because the findings are validated through mathematical proofs and simulation results, the scheme represents an early-stage research concept that requires hardware integration and testing on real-world devices before commercial use.
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Recent advances in intelligent wearable devices have brought tremendous chances for the development of healthcare monitoring system. However, the data collected by various sensors in it are user-privacy-related information. Once the individuals’ privacy is subjected to attacks, it can potentially cause serious hazards. For this reason, a feasible solution built upon the compression-encryption architecture is proposed. In this scheme, we design an Adaptive Sparse Basis Compressive Sensing (ASB-CS) model by leveraging Singular Value Decomposition (SVD) manipulation, while performing a rigorous proof of its effectiveness. Additionally, incorporating the Parametric Deformed Exponential Rectified Linear Unit (PDE-ReLU) memristor, a new fractional-order Hopfield neural network model is introduced as a pseudo-random number generator for the proposed cryptosystem, which has demonstrated superior properties in many aspects, such as hyperchaotic dynamics and multistability. To be specific, a plain medical image is subjected to the ASB-CS model and bidirectional diffusion manipulation under the guidance of the key-controlled cipher flows to yield the corresponding cipher image without visual semantic features. Ultimately, the simulation results and analysis demonstrate that the proposed scheme is capable of withstanding multiple security attacks and possesses balanced performance in terms of compressibility and robustness.
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DOI: 10.1016/j.eswa.2023.121378
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