E-healthcare System Research Articles

Privacy Preserving Analytics in Outsourced Healthcare System

The most data intensive industry today is the healthcare system. The advancement in technology has revolutionized the traditional healthcare practices and led to enhanced E-Healthcare System. Modern healthcare systems generate voluminous amount of digital health data. These E-Health data are shared between patients and among groups of physicians and medical technicians for processing. Due to the demand for continuous availability and handling of these massive E-Health data, mostly these data are outsourced to cloud storage. Being cloud-based computing, the sensitive patient data is stored in a third-party server where data analytics are performed, hence more concern about security raises. This paper proposes a secure analytics system which preserves the privacy of patients’ data. In this system, before outsourcing, the data are encrypted using Paillier homomorphic encryption which allows computations to be performed over encrypted dataset. Then Decision Tree Machine Learning algorithm is used over this encrypted dataset to build the classifier model. This encrypted model is outsourced to cloud server and the predictions about patient’s health status is displayed to the user on request. In this system nowhere the data is decrypted throughout the process which ensures the privacy of patients’ sensitive data.

PPO-CPQ: A Privacy-Preserving Optimization of Clinical Pathway Query for E-Healthcare Systems

With the help of patients’ health data, e-healthcare providers can offer reliable data services for better medical treatment. For example, the clinical pathway provides optimal detailed guidance for the clinical treatment. However, the e-healthcare providers are incompetent with the huge volumes of e-healthcare data, and a popular and feasible solution is used to outsource the medical data to powerful cloud servers (CSs). Because the medical data are very sensitive yet the outsourced servers are not fully trusted, the straightforward execution of clinical pathway query service will inevitably bring huge privacy risks to patients’ data. Apart from the privacy issues, the efficiency issues also need to be taken into consideration, such as the communication overhead and computational cost between servers and providers. Considering the above issues, this article proposes a privacy-preserving optimization of clinical pathway query scheme (PPO-CPQ) to achieve the secure clinical pathway query under e-healthcare CSs without revealing neither the private information of patients, such as name, gender, age, and physical index, nor the sensitive information of hospitals, such as treatment, medication, and expense. In our proposed scheme, it first designs secure and privacy-preserving several subprotocols, such as privacy-preserving comparison, privacy-preserving clinical comparison, privacy-preserving stage selection, and privacy-preserving stage update protocol, to ensure privacy in the e-healthcare system, then it adopts the greedy algorithm in a secure manner to perform the query and the min-heap technology to improve efficiency. The experimental result shows that our scheme is practical and efficient in terms of computational cost and communication overhead.

Cloud based security on outsourcing using blockchain in E-health systems

Recently, Electronic Health Records (EHR) plays a significant role in E-health care system that allows data exchange of patient's health records via a portal designated by healthcare professionals. In this, the lack of confidentiality and integrity factors leads to different security issues on sensitive health information and causes a serious impact on a patient's life. To accomplish the security of medical data, we propose a new scheme named Elliptical Curve Certificateless Aggregate Cryptography Signature scheme (EC-ACS) for the public verification and auditing in the Medical Cloud Server (MCS) to secure EHR using authorized blockchain technology. In this, we use Elliptic Curve Cryptography (ECC) to encrypt medical data and the Certificateless Aggregate Signature scheme (CAS) to generate the digital signature for sharing and storing data in the cloud storage. This proposed scheme ensures security, privacy and safeguards the confidential information from unauthorized access in the cloud health system. Furthermore, the blockchain technique guarantees the integrity, traceability and secure storage of medical records in the cloud environment.

Enabling Privacy-Assured Fog-Based Data Aggregation in E-Healthcare Systems.

Wearable body area network is a key component of the modern-day e-healthcare system (e.g., telemedicine), particularly as the number and types of wearable medical monitoring systems increase. The importance of such systems is reinforced in the current COVID-19 pandemic. In addition to the need for a secure collection of medical data, there is also a need to process data in real-time. In this article, we design an improved symmetric homomorphic cryptosystem and a fog-based communication architecture to support delay- or time-sensitive monitoring and other-related applications. Specifically, medical data can be analyzed at the fog servers in a secure manner. This will facilitate decision making, for example, allowing relevant stakeholders to detect and respond to emergency situations, based on real-time data analysis. We present two attack games to demonstrate that our approach is secure (i.e., chosen-plaintext attack resilience under the computational Diffie-Hellman assumption), and evaluate the complexity of its computations. A comparative summary of its performance and three other related approaches suggests that our approach enables privacy-assured medical data aggregation, and the simulation experiments using Microsoft Azure further demonstrate the utility of our scheme.

Dynamic power optimization for secondary wearable biosensors in e-healthcare leveraging cognitive WBSNs with imperfect spectrum sensing

The integration of cognitive radio with e-healthcare systems assisted by wireless body sensor networks (WBSNs) has been regarded as an enabling approach for a new generation of pervasive healthcare services, to provide differentiated quality of service requirements and avoid harmful electromagnetic interference to primary medical devices (PMDs) over the crowded radio spectrum. Due to the sharing spectrum bands with PMDs in e-healthcare scenario using cognitive WBSNs (CWBSNs), efficient transmit power control and optimization strategies for resource-constrained secondary wearable biosensors (SWBs) play a key role in controlling the inter-network interference and improving the energy efficiency. This paper investigates the problem of dynamic power optimization for SWBs in e-healthcare leveraging CWBSNs with practical limitations, e.g., imperfect spectrum sensing and quality of physiological data sampling. We develop a distributed optimization framework of dynamic power optimization via the theory of differential game, by jointly considering utility maximization and quality of physiological data sampling for every SWB, while satisfying the evolution law of energy consumption in SWB’s battery. With the non-cooperation and cooperation relations for all SWBs in mind, we transform the differential game model into two subproblems, namely, utility maximization problem and total utility maximization problem. Utilizing Bellman’s dynamic programming, we derive a non-cooperative optimal solution for power optimization as a Nash equilibrium point for the utility maximization problem posed by competitive scenario. By exploiting Pontryagin’s maximum principle, a cooperative optimal solution is obtained for the total utility maximization problem, wherein all SWBs fully cooperate to obtain the highest total utilities. Building upon the analytical results, the actual utility distributed to each SWB is compared between the non-cooperative and cooperative schemes. Extensive simulations show that the proposed optimization framework is indeed an efficient and practical solution for power control compared with the benchmark algorithm.

An improved user authentication scheme for electronic medical record systems

Electronic Medical Record (EMR) systems is a part of e-healthcare system, which is developing rapidly. In this, it is possible to deliver medical services among multiple participants over a network without physical presence. Since sensitive data is transmitted over public channels, it is very much required to maintain the secrecy of that data. This is achieved by mutual authentication between the participants. For this, various schemes for authentication with smart cards have been proposed. Han et al. proposed one such biometrics-based scheme for the same purpose using hash functions along with symmetric key encryption and elliptic curve cryptography. From cryptanalysis of their scheme, we have pointed out weaknesses viz. no user anonymity, user and server impersonation, man-in-the-middle attack. These security issues have been presented in this article. To overcome these attacks, a scheme has been proposed in this article. Since it does not use symmetric key encryption, the proposed scheme reduces the computational complexity as can be seen in the comparison provided. The security analysis of the proposed scheme, along with BAN (Burrows-Abadi-Needham) logic has been explained in detail. Comparison of the proposed scheme with related schemes with respect to computation cost, execution time and performance is demonstrated. This proves that the proposed scheme performs well in terms of security as well as computational efficiency.

Joint optimization of power control and time slot allocation for wireless body area networks via deep reinforcement learning

E-healthcare system based on wireless body area network (WBAN) promises to produce potential benefits in health-care industry. A major issue of such an on-body networked system is the energy efficiency, that is, how to improve the reliability and effectiveness of physiological data transmission with the energy constraints of tiny wireless sensors. Motivated by this, we consider an individual WBAN scenario, focusing on finding an adaptive time slot allocation and power control scheme to maximize the average energy efficiency for implementing the task of health monitoring. We formulate the maximization problem with latency and sensors’ energy budget constraints as a markov decision process (MDP). As a solution, we propose a deep reinforcement learning-based scheme to make a sequence decision for the MDP, which jointly optimizes power control and slot allocation. Simulation results show that the proposed scheme is energy efficient and has a good convergence.

Embedding in medical images: an efficient scheme for authentication and tamper localization

In e-healthcare applications integrity of the received information is of prime importance for ensuring the accurate diagnosis. The integrity of electronic medical record (EMR) is possible only when the medical images and other relevant data received are tamper free. Reversibility of medical images after certain degree of processing is always a desired property in medical images as it aids proper diagnosis. This paper proposes a novel reversible image authentication (RIA) scheme for tamper detection and authentication of medical images. In the proposed RIA scheme, the medical image is divided into 4 × 4 blocks followed by embedding fragile watermark bit (for authentication) in each of these blocks. Along with authentication, the localization of tamper is achieved accurately by using LSB embedding along with mean modification approach. The security of the watermark has been enhanced with Arnold cat map, Gray coding and AES-128 encryption. The experimental results show that the scheme offers better fragility against all intentional/unintentional signal processing attacks. The comparison results between proposed RIA scheme and similar state-of-the-art schemes show superiority of our scheme in terms of imperceptivity, fragility and tamper localization. For a payload of 16 kb, the average PSNR achieved for 50 Gray scale images of size 512 × 512 is more than 47 dB. In addition, the scheme offers very less complexity; the embedding and extraction times are around 0.6 s and 0.5 s respectively. Given the features of proposed scheme it could serve as a potential candidate for transfer of EMR in an e-healthcare system.

Authentication using Elliptical Curve Cryptography for e-Healthcare System

Medical record's security attracts a lot of attention these days. Whatever the security of health care improves, its application becomes wider. Li et al. marked this issue by proposing a new privacy preserving RFID authentication protocol for e-health care.[1] Medical systems allow various services and ensures privacy concerns. Wu et al. planned a card-based authentication scheme for medical systems. With the development in technology, doctors can provide better services, via various modes, by using mobile, computers, wireless devices both doctors and patients will be able to keep up with their medical condition. Moreover, information related to health care is bit sensitive and needs encrypted transfer, storage. A lot of schemes are being proposed against concerned security attacks. In this paper, we have proposed protocol which can be used to authenticate both client and server end, which will ensure safe transfer and will manage the time and cost of computation.

Cybercrimes: Threats, Challenges, Awareness, and Solutions in Sierra Leone

The internet hosts all online activities either for public or private usage. It is a powerful online podium where people socialize, make new friends, conduct academic research, perform online business transactions, share sensitive data, communication over the internet, surveillance purpose by security agencies, monitor climatic condition, used in e-healthcare system, online banking, online pay, e-commerce, defense system, and host of others critical infrastructures are the new order of the day. This has attracted online criminals to diverse numerous malicious techniques to invade people’s privacy and also exploit those data. A new game has resulted in these online malicious activities are known as “cybercrime or internet crime” which is a rewarding business as of today. Therefore, it has become difficult to protect those online activities as cybercrimes are growing daily, which suggests that effective and appropriate countermeasures are needed to combat those threats and make online activities more secure. The research paper presents the various types of cybercrime activities, countermeasures, and suggestions for online users.

Lecr: Less Energy Consumption Routing in Internet of Things

Internet of Things (IoT) constitutes a network of various devices has an equipment with the mandatory facility of communication and optional facilities of sensing, information collecting, storage and processing. IoT network has been used for research and development purpose in many application areas such as military environment, traffic management, and e-healthcare system. IoT network was enormous in scale and complexity, mainly in terms of energy efficiency because battery lifetime is limited. The previous routing protocols for IoT are difficult and require a huge memory use and high energy consumption which are insufficient for IoT network processing. For that reason, an efficient routing algorithm needed to decrease energy consumption while communication. To tackle this problem, this paper proposes Less Energy Consumption Routing (LECR) algorithm. This algorithm reduces energy consumption using 4 ways in IoT, (1) Sleep and Wake up Scheduling, (2) Route Discovery in IoT Base Station (3) Less Power Consumption Route for Communication (4) Reduce Overhead while Routing. The experimental result proves the LECR algorithm reduces IoT devices battery drain and increases lifetime of the IoT network efficiently.

Securing and authenticating healthcare records through blockchain technology

Healthcare data is important in making critical policy decisions, patients care, and medical diagnostics to name a few. Due to the importance and market demand, healthcare data is also vulnerable to cyber attacks. The centralized record keeping systems expose a single node for the attackers to attack. A decentralized system is computationally expensive but has the ability to be revolutionary by keeping the patient at the core and providing security, transparency, privacy, and interoperability of the electronic healthcare data. A blockchain is such an implementation of a distributed and decentralized system using reliable cryptographic algorithms. This paper proposes a secure blockchain based architecture tailored specifically to cater to the needs of e-healthcare systems.

Blockchain for IoT-Based Healthcare: Background, Consensus, Platforms, and Use Cases

Internet of Things (IoT) and blockchain technologies are being heavily exploited and used in may domains, especially for e-healthcare. In healthcare, IoT devices have the ability to provide real-time sensory data from patients to be processed and analyzed. Collected IoT data are subjected to centralized computation, processing, and storage. Such centralization can be problematic, as it can be a single point of failure, mistrust, data manipulation and tampering, and privacy evasion. Blockchain can solve such serious problems by providing decentralized computation and storage for IoT data. Therefore, the integration IoT and blockchain technologies can become a reasonable choice for the design of a decentralized IoT-based e-healthcare systems. In this article, first, we give a brief background on blockchain. Second, popular consensus algorithms used in blockchain are discussed in the context of e-health. Third, blockchain platforms are reviewed for their appropriateness in IoT-based e-healthcare. Finally, few use cases are methodologically given to show how key features of the IoT and blockchain can be leveraged to support healthcare services and ecosystems. We also propose a data-flow architecture that combines the IoT with blockchain, called IoBHealth, that can be used for storing, accessing, and managing of e-healthcare data.

A Dynamic Three-Bit Image Steganography Algorithm for Medical and e-Healthcare Systems

Massive advances in internet infrastructure are impacting e-healthcare services compared to conventional means. Therefore, extra care and protection is needed for extremely confidential patient medical records. With this intention, we have proposed an enhanced image steganography method, to improve imperceptibility and data hiding capacity of stego images. The proposed Image Region Decomposition (IRD) method, embeds more secret information with better imperceptibility, in patient's medical images. The algorithm decomposes the grayscale magnetic resonance imaging (MRI) images into three unique regions: low-intensity, medium-intensity, and high-intensity. Each region is made up of k number of pixels, and in each pixel we operate the block of n least significant bits (LSBs), where 1 ≤ n ≤ 3. Four classes of MRI images of different dimensions are used for embedding. Data with different volumes are used to test the images for imperceptibility and verified with quality factors. The proposed IRD algorithm is tested for performance, on the set of brain MRI images using peak signal-to-noise ratio (PSNR), mean square error (MSE) and structural similarity (SSIM) index. The results elucidated that the MRI stego image is imperceptible, like the original cover image by adjusting 2 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">nd</sup> and 1 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">st</sup> LSBs in the low-intensity region. Our proposed steganography technique provides a better average PSNR (49.27), than other similar methods. The empirical results show that the proposed IRD algorithm, significantly improves the imperceptibility and data embedding capacity, compared to the existing state-of-the-art methods.

An Enhanced Authentication Scheme for Internet of Things Based E-Healthcare System

Security is a major challenge in modern IoT based healthcare monitoring systems. It provides many benefits such as critical patient monitoring, remote diagnosis at anytime, anywhere. Hence, security of this data is essential when the healthcare professionals access it. Also, while storing the patients record; it must be kept safe from misuse and modification of data as other devices can easily track it. To prevent this type of threats, we have proposed a mutual authentication protocol to enhance health care security and to resist vulnerable attacks. The proposed scheme used Challenge response protocol for the authentication purpose and the Diffie-Hellman key exchange protocol is used for generation of the session key generation. The security analysis of the proposed scheme shows that the scheme is more secure and resist all the major attacks as compared to other schemes. The Formal verification of this schema also ensures that it resists most probable attacks in this system. The result of the proposed authentication scheme shows that it has low computational and communicational load.

An enhanced two factor authentication for e-healthcare system

An enhanced two factor authentication for e-healthcare system

An enhanced two factor authentication for e-healthcare system

The wireless network technology with its gradual advancements is adapted by many domains like banking, governance, health services, etc., where most of their transactions include customer's sensitive data. Along with advancement in the network technologies, digital crime is also increased at high rate. This may cause loss of sensitive data through malicious activities in the network. This concern of network security system can be effectively handled through the strong authentication mechanism. It can be observed that multiple authentication mechanisms that are currently used in the wireless digital network are vulnerable towards various advanced malicious techniques. Hence, there is a need to strengthen the existing authentication mechanisms or to design a stronger authentication mechanism. This paper considers the vulnerabilities identified in a two-factor authentication scheme for online medical services proposed by Li et al. and further proposes an enhanced two-factor authentication that overcomes the vulnerabilities analysed from Li et al. mechanism.

A Detailed Review for the E-healthcare Management System and Framework in South Korea

A Detailed Review for the E-healthcare Management System and Framework in South Korea

Fog Computing Employed Computer Aided Cancer Classification System Using Deep Neural Network in Internet of Things Based Healthcare System.

Computer assisted automatic smart pattern analysis of cancer affected pixel structure takes critical role in pre-interventional decision making for oral cancer treatment. Internet of Things (IoT) in healthcare systems is now emerging solution for modern e-healthcare system to provide high quality medical care. In this research work, we proposed a novel method which utilizes a modified vesselness measurement and a Deep Convolutional Neural Network (DCNN) to identify the oral cancer region structure in IoT based smart healthcare system. The robust vesselness filtering scheme handles noise while reserving small structures, while the CNN framework considerably improves classification accuracy by deblurring focused region of interest (ROI) through integrating with multi-dimensional information from feature vector selection step. The marked feature vector points are extracted from each connected component in the region and used as input for training the CNN. During classification, each connected part is individually analysed using the trained DCNN by considering the feature vector values that belong to its region. For a training of 1500 image dataset, an accuracy of 96.8% and sensitivity of 92% is obtained. Hence, the results of this work validate that the proposed algorithm is effective and accurate in terms of classification of oral cancer region in accurate decision making. The developed system can be used in IoT based diagnosis in health care systems, where accuracy and real time diagnosis are essential.

Internet Connected e-Healthcare System with Live Video Monitoring using LWIP Stack and SJF Priority Scheduling

Continuous monitoring of patients in critical condition becomes essential in medical field. This paper aims at developing an innovative IoT based remote healthcare monitoring system by using Free RTOS with priority scheduling based on SJF (Shortest Job First). Proposed system offers vital medical information and live video of a patient who is present in rural area. It can make easy and quick accessing of a patient by medical professional from remote areas. Also helps for detailed analysis and taking correct remedies to effectively cure that patient. Various health parameters such as patient's body temperature, heart rate, body position along with live video are been monitored through internet using this system. In Free RTOS used in programming, SJF based priority scheduling is chosen because of reduced average waiting and turnaround time of CPU on performing multiple tasks associated with the camera and other sensors. The internet connectivity part of program is built using LWIP TCP/IP stack in focus of limited microcontroller resource.