Blockchain-enabled Security for Medical Image Transmission: Prescription Data Hiding and Multi-secret Sharing-based Encryption

Abstract

Medical images bear sensitive patient information, making their transmission a security concern. The privacy and security of such graphical representations and incidental patient information in transit via public networks must be preserved. Medical images contain sensitive information, which sets them apart from ordinary images. Medical images are more sensitive and contain crucial information. This leads to a reliance on more secure techniques than conventional methods like cryptography and data hiding, which normally take more time and security. In this chapter, we propose implementing two innovative techniques to enhance the security of medical data sharing: Prescription information concealed in medical images and secure and share-prescription using a multi-secret sharing blockchain. Prescription data hiding, on the other hand, refers to the encryption of prescription details within normal-looking images like X-ray or MRI scans, among others. Incorporating sensitive data into the images makes it difficult for unauthorized persons to access them. Additionally, we build on the potential of using a blockchain, an immovable and distributed database, to share crucial clinical information safely. Medical data is kept through a blockchain database, which spreads the data around a network. It becomes harder for attackers to tamper or alter the data using traditional methods. Smart contracts also add security to data sharing by enabling data to be available only to the relevant parties, which gives security an extra layer. As a novel solution to solve the serious security issues of medical data sharing, the proposed scheme involves prescription data hiding in medical images, multi-secret sharing-based encryption, and the security properties of the blockchain. Our proposed techniques ensure the privacy and integrity of patients' data when transmitting medical images.

INTRODUCTION

Medical data transmission is an important procedure that involves transferring medical images through public networks and ascribes a massive priority to security measures. Such images bear very sensitive patient details; therefore, there is a need to ensure that their transfer is very secure. Several internal traditional data security approaches, like cryptography and data hiding, possess some drawbacks regarding time consumption and the degree of protection they offer to medical image applications. Thus, this project will provide a solution to increase the security of medical images through the security solution enabled by blockchain, prescription data embedding, and multi-secret sharing encryption. The project aims to solve the problems of non-trivial protection of medical image transmission, patient data confidentiality, and data integrity. Medical images are entirely unlike standard images because they contain a large volume of tremendously important data. Therefore, effective measures to enhance security, apart from other traditional methods and techniques, need to be drawn up.

Prescription data in medical images is a combination of concealing prescription data within the medical image using a steganographic approach. This approach provides an extra layer of protection to the greater mass of data incorporated into the images. Through steganography, there are ways that unauthorized people will not be easily able to see what is hidden; hence, the data's privacy and confidentiality will be intact. In addition, for the remainder of the project, the technology being applied is Blockchain, a distributed, unalterable ledger. Due to the distributed nature of Blockchain, clinical data is thus highly robust in terms of issues of hacking or alteration by unauthorized persons. This makes it difficult for the attackers to modify or foolproof the images transmitted by this technology.

For higher security, build multi-secret sharing-based encryption into the project. This encryption divides the security data into portions distributed among the approved parties. The raw data can be disaggregated only when authorized parties merge their shares. It also has a security feature that prevents anyone unauthorized from making any changes or accessing the data in question. This project aims to provide a multi-faceted solution to the problem of insecure medical image transmission. Solutions such as hiding prescription details in the medical images, the use of Blockchain function, and multi-secret sharing-based encryption ensure the enhanced security of patients' information within the framework of the project. Applying these techniques ensures the medical images' security during transmission across public networks while remaining intact and private. Fig. (1) shows the concept of managing healthcare data through blockchain security for medical image transfer. It comprises several parts and their relationships to secure an efficient healthcare data management.

Objectives of this Chapter

• Implement Prescription Data Hiding: In general psychopathology, using steganography, it is possible to create approaches to hide prescription data in medical images, such as X-rays or MRI scans. This makes the process secure since it is difficult for unauthorized personnel to penetrate the hidden images.
• Utilize Blockchain Technology: Some of the best use cases can be using blockchain technology's decentralized and tamper-proof features to store and share medical data. With the project's blockchain implementation, it becomes challenging for attackers to manipulate the data transmitted or stored as images.
• Enhance Security with Multi-secret Sharing-based Encryption: This includes the encryption concept that divides information into several parts and distributes the segments to relevant users. This method ensures that only proper people can restore the information, thus adding another layer of security.

The research is expected to achieve the following objectives, hence developing a broader security framework for medical image transmission. It aims to ensure patient information's privacy, confidentiality, and integrity and alleviate the risks of transferring medical images over public networks.

RELATED WORKS

The authors of the presented work [1] provide a detailed review of various fields in the healthcare system that utilize security and privacy-oriented methods. It also looks at the related concerns. Further, the research outlines ways of achieving secure and privacy-preserving machine learning for healthcare applications. The research involves a literature review of the present and past contributions, a discussion of the security and reliability of ML and DL models used for healthcare systems' development, and a focus on the dimensions above. The first research question concerns different security issues that may emerge when using ML and DL in healthcare. Besides highlighting the security and robustness issues accompanying the usage of ML and DL, the research briefly reviews general threats and sources of risks that hinder the safe and reliable integration of ML and DL into healthcare applications. Different privacy and security issues must be solved to achieve reliable and secure usage of these models within the clinical context.

Also, the features of applying cryptography as an algorithm in healthcare are investigated in the research. The advantage of cryptography is that it can secure data and information exchanged over the phone or any other communication medium against any attempt to get hold of them. However, it is necessary to point out that the employment of cryptography is closely connected with high costs, and this may become an adverse factor in some cases.

The proposed system [2], uses the Henon chaotic map, Brownian motion, and Chen's chaotic system to make a multiple-stage encryption algorithm. This is true because of the integration of chaos theory with Brownian motion and Chen's chaotic system, which makes the scheme secure for storage systems in hospitals and medical centers. Randomness in the encryption process is created using a two-dimensional Henon chaotic map, while diffusion is created using Brownian motion and Chen's chaos system.

The reliability of...