Risk monitoring strategy for confidentiality of healthcare information

Highlights

• Propose an approach for securing healthcare data confidentiality.

• Provide mathematical modelling for the entire working of Modular Encryption Standard.

• Enhance the decision-making strategy of Modular Encryption Standard using fuzzy logic.

Abstract

Concrete privacy endeavours to give Confidentiality, Integrity, and Availability (CIA) measures to secure traffic streams in sensitive healthcare applications are a necessity. When talking about the access of sensitive healthcare data or the confidentiality of highly esteemed data, the first thing that comes to mind is that it should be secured. Sensitive healthcare data needs to be protected to restrict illegal access, exposure, and/or manipulation. As there is no such protection by which we can make our systems fully secure, the most acceptable methodology is to perform layered modelling of safety measures. This paper intends to provide a mathematical description of the Modular Encryption Standard (MES) and the augmentation of condition-centric risk monitoring of confidential information to provide a layered model for securing healthcare data confidentiality. Decision-making regarding the risk monitoring strategy of MES is augmented using a machine learning approach based on a Fuzzy Inference System amalgamated with Neural Networks. Result analysis shows that MES has less than a 0.005 error-rate and a 97% precision-rate, which elucidates its desideratum towards increasing security risks.

Introduction

With the extensive technological advancements in Industry 4.0 applications, sensitive healthcare information is increasingly defenceless to be accessed, copied, and/or manipulated by unauthorized users [1], [2]. Digital reports and documents can be accessed by numerous intruders, as it becomes fairly simple to disperse and duplicate them. Hugeness, exactness, and affectability of data (in the cloud) is a significant security and protection issue, making it critical to guarantee data confidentiality [3], [4], [5]. There are immense benefits of utilizing data storage of critical cloud infrastructure:

• universal access: anywhere, in any case, whenever anyone wants access to information

• extraordinarily reliable provision of services

• versatility

• adaptability

• cost-effectiveness

Distributed computing models are ordinarily known as cloud computing and incorporate different resources and services (being part of industrial applications). For example, Infrastructure as a Service (IaaS), where a client utilizes supplier’s processing, storage, or system’s infrastructure administration; Platform as a Service (PaaS), where a client uses the supplier’s assets to run custom applications; lastly Software as a Service (SaaS), where clients use the programming that runs on the supplier’s infrastructure. Secrecy and protection have turned into a pressing concern. Security can be regarded as an “affirmation of being safe or protected from hazards”. With technological advancements, threats against network security are a significant concern these days in Industry 4.0 applications, and transmission media need to be secured [6]. Previous works have been proposed against the assurance of data confidentiality [7]. The CIA triad is made up of Confidentiality, Integrity, and Availability [8], which needs to be ensured. These three terms are considered as the key constructs for security measures. These constraints are shown in Fig. 1 for data preservation.

Confidentiality is all about access restriction. Integrity guarantees data accuracy and trustworthiness, and availability is the assurance of access restriction by authentic users, and it is the similitude of privacy. Distinct security approaches are proposed to prevent sensitive data from illegitimate users but with the assurance of access for legitimate users. Confidentiality maintenance failure results in illegal access to data either intentionally or unintentionally. These kinds of failures are regarded as security breaches. Moreover, if your data is publicized, you can never keep it secret. For example, if the records of a bank are somehow disclosed, then everyone will have information about your bank balance and account number. Enciphering data is a usual way to ensure confidentiality. It constitutes that data should be accurate, consistent, and trustworthy and the guaranteed authenticity of its origin. For example, when someone runs an e-commerce website and a hacker hacks the website and spitefully manipulates the price of products. Therefore, this integrity failure will result in price changes. Availability leads us to the concept that data should be approached and only available to the authentic user.

The central issue around healthcare information protection is data security from unapproved access [9]. In any situation, some of the issues are unacceptable, i.e., the danger of healthcare information being revealed, lost, corrupted, or stolen. Despite its favourable circumstances, distributed storage brings many security issues. Data confidentiality is the most significant problem for clients of a distributed storage framework [10]. Customers’ healthcare information is overseen out of their control as data is outsourced to third parties, so the most significant threat is that the sensitive data may become compromised. The outsourced data may incur threats from insiders as well as outsiders. Despite all these benefits, security issues that should be considered serious include

• unapproved access

• sensitive healthcare information exposure

• Intellectual Property Rights (IPR)

• risks in exchanging healthcare information

• healthcare information trustworthiness.

Cloud information security is a prominent issue of cloud computing [11], [12]. Before enterprises and agencies outsource their healthcare information to cloud-Service-Providers (CSPs), data must be encrypted to secure it from even internal service providers. The core idea of a secure storage scheme is the encryption of healthcare information before sending it outside of trustworthy distributed storage. Various encryption algorithms at the edge have been used to secure data [13]. In this work, an adaptive neuro-fuzzy inference system (ANFIS) is utilized with MES that decides the proper security measures based on the degree of sensitivity of the confidential data.

Various machine learning (ML) algorithms have been used to achieve an appropriate level of security [14]. However, utilizing an ML approach is crucial for any autonomous decision-making approach for requirement-centric security provisions (according to data sensitivity level). Accordingly, the decision-making approach of MES is made more robust and autonomous using ML algorithms.

This paper is intended to enhance the risk monitoring approach of MES using an ML approach. The increasing demand for distributed computing raises numerous issues of data threats in cloud computing. The major contributions of this research are as follows:

• Perform the amalgamation of ML and cryptography encryption algorithms to attain a more robust outcome using a context-aware threat sensing approach.

• Provide mathematical modelling for the entire working of MES.

• Enhance the decision-making strategy of MES using fuzzy logic (against the degree of sensitivity of the data and the respective provision of security by MES). Using fuzzy logic, the decision-making of MES is made extensively robust (i.e.based on the degree of truthiness).

The rest of the paper is arranged as follows. Section 2 presents the literature review of previous commonly used symmetric encryption algorithms used for Industry 4.0 applications. Section 3 discusses the utilized algorithm. Section 4 presents the mathematical description of MES. Section 5 elucidates the experiments performed and discusses the experimental results. Finally, Section 6 concludes the paper and expounds the future work as well.