Cloud-based Healthcare System Research Articles

SimPy Simulation to Optimize Remote Patient Monitoring and Server Usage in Healthcare System

The recent pandemic has shown us the necessity of handling a huge mass of patients in real-time scenarios. This is important because in real-time scenarios when the entire healthcare system is operating in its full swing to deliver services to the patient, it is equally important to monitor the arrival time of the patient at the hospital till the disposal of a patient from the hospital. Hence, that each patient must wait minimum time frame there by using the existing remote healthcare infrastructure. To perform this micro level (i.e. patient level) monitoring of the remote healthcare ecosystem, an integrated cloud-based healthcare system (CHS) should be proposed which will analyse, simulate, and predict a huge load of service requests of a patient. At the same time, it can prepare the entire healthcare ecosystem to face the real-time need of any nation and deliver corresponding service response to every patient in a fastest possible manner. In this regard, Discrete event simulation in SimPy (DES) methodology is of utmost help to the amplification of remote healthcare infrastructure on a real scale. In this report, authors have found 80 percent server efficiency and 1.4 s waiting time average for each patient in the proposed model. Server efficiency and patient waiting time mapping typically has become very promising action for any remote cloud-assisted healthcare service delivery model. Idle time analysis of servers can stimulate remote resource utilization and scalability in patient modelling.

Securing Endpoint API Integration in Cloud-Based Healthcare Systems: Challenges, Solutions, and Future Directions

Securing Endpoint API Integration in Cloud-Based Healthcare Systems: Challenges, Solutions, and Future Directions

Dynamic Data Placement Strategy with Network Security Issues in Distributed Cloud Environment for Medical Issues: An Overview

: The rapid integration of distributed cloud systems in the healthcare industry has profoundly impacted the management of valuable medical data. While this advancement has significantly improved data handling, protecting sensitive healthcare information in such a complex environment remains daunting. This comprehensive study explores the crucial intersection between dynamic data placement strategies and network security concerns in distributed cloud environments, particularly healthcare. After establishing the significance and context of this research, the survey delves into the growing need to safeguard medical data within the everevolving landscape of cloud-based healthcare systems. It lays out fundamental concepts, such as dynamic data placement and network security, highlighting their unique implications in the medical domain. Ultimately, this survey sheds light on the most effective approaches for balancing dynamic data placement and network security in the healthcare sector. This research delves into examining many tactics, evaluating their effectiveness in handling delicate medical information, and presenting tangible use cases. A key focus of this investigation is the fusion of data organization and network safety within the healthcare industry. It investigates the adaptability of dynamic data positioning techniques in fortifying network security and safeguarding against potential threats unique to the healthcare sector. Case studies of the successful implementation of these strategies in healthcare establishments are also included.

Efficient Resource Scheduling in Cloud-based Healthcare Systems

Cloud computing is becoming a key tool in the quickly changing healthcare industry, bringing about major changes to contemporary medical procedures. This study examines the various applications of cloud computing in the healthcare industry, highlighting how it can improve patient care, simplify processes, and stimulate creativity. Healthcare providers can enhance data management, interoperability, and scalability, resulting in more effective and efficient clinical procedures, by utilizing cloud-based solutions. The article explores important uses, emphasizing how cloud computing enables these developments, including electronic health records (EHRs), telemedicine, big data analytics, and AI-driven diagnostics. In addition, it presents a new effective load-balancing architecture for resource scheduling in cloud-based healthcare communications, guaranteeing maximum resource efficiency and continuous service provision. In addition, this paper discusses important issues including data security, privacy, and regulatory compliance while providing advice on how to reduce the risks involved. This paper highlights the transformative impact of cloud computing on healthcare delivery and illustrates its role in changing medical practices of the future through a thorough review of current trends, innovations, and case studies.

ZTCloudGuard: Zero Trust Context-Aware Access Management Framework to Avoid Medical Errors in the Era of Generative AI and Cloud-Based Health Information Ecosystems

Managing access between large numbers of distributed medical devices has become a crucial aspect of modern healthcare systems, enabling the establishment of smart hospitals and telehealth infrastructure. However, as telehealth technology continues to evolve and Internet of Things (IoT) devices become more widely used, they are also increasingly exposed to various types of vulnerabilities and medical errors. In healthcare information systems, about 90% of vulnerabilities emerge from medical error and human error. As a result, there is a need for additional research and development of security tools to prevent such attacks. This article proposes a zero-trust-based context-aware framework for managing access to the main components of the cloud ecosystem, including users, devices, and output data. The main goal and benefit of the proposed framework is to build a scoring system to prevent or alleviate medical errors while using distributed medical devices in cloud-based healthcare information systems. The framework has two main scoring criteria to maintain the chain of trust. First, it proposes a critical trust score based on cloud-native microservices for authentication, encryption, logging, and authorizations. Second, a bond trust scoring system is created to assess the real-time semantic and syntactic analysis of attributes stored in a healthcare information system. The analysis is based on a pre-trained machine learning model that generates the semantic and syntactic scores. The framework also takes into account regulatory compliance and user consent in the creation of the scoring system. The advantage of this method is that it applies to any language and adapts to all attributes, as it relies on a language model, not just a set of predefined and limited attributes. The results show a high F1 score of 93.5%, which proves that it is valid for detecting medical errors.

Cloud-based secure data storage and access control for internet of medical things using federated learning

PurposeThe purpose of this paper Computing is a recent emerging cloud model that affords clients limitless facilities, lowers the rate of customer storing and computation and progresses the ease of use, leading to a surge in the number of enterprises and individuals storing data in the cloud. Cloud services are used by various organizations (education, medical and commercial) to store their data. In the health-care industry, for example, patient medical data is outsourced to a cloud server. Instead of relying onmedical service providers, clients can access theirmedical data over the cloud.Design/methodology/approachThis section explains the proposed cloud-based health-care system for secure data storage and access control called hash-based ciphertext policy attribute-based encryption with signature (hCP-ABES). It provides access control with finer granularity, security, authentication and user confidentiality of medical data. It enhances ciphertext-policy attribute-based encryption (CP-ABE) with hashing, encryption and signature. The proposed architecture includes protection mechanisms to guarantee that health-care and medical information can be securely exchanged between health systems via the cloud. Figure 2 depicts the proposed work's architectural design.FindingsFor health-care-related applications, safe contact with common documents hosted on a cloud server is becoming increasingly important. However, there are numerous constraints to designing an effective and safe data access method, including cloud server performance, a high number of data users and various security requirements. This work adds hashing and signature to the classic CP-ABE technique. It protects the confidentiality of health-care data while also allowing for fine-grained access control. According to an analysis of security needs, this work fulfills the privacy and integrity of health information using federated learning.Originality/valueThe Internet of Things (IoT) technology and smart diagnostic implants have enhanced health-care systems by allowing for remote access and screening of patients’ health issues at any time and from any location. Medical IoT devices monitor patients’ health status and combine this information into medical records, which are then transferred to the cloud and viewed by health providers for decision-making. However, when it comes to information transfer, the security and secrecy of electronic health records become a major concern. This work offers effective data storage and access control for a smart healthcare system to protect confidentiality. CP-ABE ensures data confidentiality and also allows control on data access at a finer level. Furthermore, it allows owners to set up a dynamic patients health data sharing policy under the cloud layer. hCP-ABES proposed fine-grained data access, security, authentication and user privacy of medical data. This paper enhances CP-ABE with hashing, encryption and signature. The proposed method has been evaluated, and the results signify that the proposed hCP-ABES is feasible compared to other access control schemes using federated learning.

Privacy-Enhanced Heart Disease Prediction in Cloud-Based Healthcare Systems: A Deep Learning Approach with Blockchain-Based Transmission

The increasing adoption of cloud computing in healthcare presents immense opportunities for disease prediction, while raising critical privacy concerns. This study proposes a novel privacy-preserving scheme that leverages advanced cryptographic techniques, blockchain technology and deep learning approach within a cloud platform, to ensure secure data handling and accurate disease prediction. The proposed methodology encompasses authentication, encryption, blockchain-based transmission, and a deep learning-based heart disease prediction system (HDPS). Through rigorous authentication protocols and two-level security mechanisms, patient data is securely encrypted using RSA and Blowfish encryption before storage in the cloud. Blockchain technology facilitates secure data transmission, ensuring integrity and traceability. At the receiver end, data decryption precedes input into the HDPS, comprising artificial neural networks (ANN), convolutional neural networks (CNN), and recurrent neural networks (RNN). The HDPS incorporates data preprocessing, feature extraction, feature selection, and a deep learning-based prediction model, achieving remarkable accuracy (0.9941) in heart disease prediction. Implemented in MATLAB, this approach offers a robust framework for privacy-preserving heart disease prediction in cloud-based healthcare systems.

Secure and scalable data aggregation techniques for healthcare monitoring in WSN

Wireless Sensor Networks (WSN) are crucial in the healthcare field as they allow for the immediate monitoring and collection of data from medical sensors. The growing incorporation of WSN in healthcare requires the creation of authentication protocols that are both strong and energy-efficient. This study presents Lightweight Authentication Protocol for Cloud-based Health-care Systems (LAPCHS) using BLAKE2 designed specifically for WSN in medical settings for secure and scalable data aggregation. The proposed method tackles the difficulties presented by limited resources in sensor nodes, with the goal of achieving a careful equilibrium between strong security and effective energy usage. LAPCHS utilizes innovative techniques to improve the energy efficiency in WSN. The system employs the BLAKE2 cryptographic hash algorithm to establish robust mutual authentication and key agreement between the tag and the cloud server. The protocol is characterized by its low weight and high efficiency, rendering it appropriate for deployment in medical sensor networks with limited resources, which minimizes the need for global key distribution and decreases communication overhead. The proposed method is useful to meet the distinct needs of medical sensor networks, where energy preservation is of utmost importance to ensure extended device scalability. The findings demonstrate that LAPCHS surpasses current approaches in terms of energy efficiency and latency, while simultaneously upholding a robust level of security. The protocol’s novel strategy for managing encryption keys and selecting cryptographic algorithms makes it a promising solution for securing medical sensor networks while maintaining energy sustainability. This study makes a valuable contribution to the developing field of WSN security by proposing a practical solution for ensuring strong and energy-efficient authentication in healthcare applications.

Verifiable attribute-based keyword search scheme over encrypted data for personal health records in cloud

Personal health record (PHR) is a medical model in which patients can upload their medical records and define the access control by themselves. Since the limited local storage and the development of cloud computing, many PHR services have been outsourced to the cloud. In order to ensure the privacy of electronic medical records, patients intend to encrypt their health records before uploading them. However, encrypted PHR can not be accessed directly and not be retrieved by legitimate users. To solve these issues, in this article we propose a new searchable encryption scheme with ciphertext-policy attributes, which achieves fine-grained access control and exact keyword search over encrypted PHRs. Moreover, in our proposed scheme, the receiver can verify the integrity of the search result that the cloud server returns. Finally, we simulate our scheme, and the experiments show that our scheme has high practicability for cloud-based healthcare systems and has high efficiency in aspects of keyword search and results verification.

Securing Patient Medical Records with Blockchain Technology in Cloud-based Healthcare Systems

Securing Patient Medical Records with Blockchain Technology in Cloud-based Healthcare Systems

Integrated Privacy Preserving Healthcare System Using Posture-Based Classifier in Cloud

Privacy-preserving online disease prediction and diagnosis are critical issues in the emerging edge-cloud-based healthcare system. Online patient data processing from remote places may lead to severe privacy problems. Moreover, the existing cloud-based healthcare system takes more latency and energy consumption during diagnosis due to offloading of live patient data to remote cloud servers. Solve the privacy problem. The proposed research introduces the edge-cloud enabled privacy-preserving healthcare system by exploiting additive homomorphic encryption schemes. It can help maintain the privacy preservation and confidentiality of patients’ medical data during diagnosis of Parkinson’s disease. In addition, the energy and delay aware computational offloading scheme is proposed to minimize the uncertainty and energy consumption of end-user devices. The proposed research maintains the better privacy and robustness of live video data processing during prediction and diagnosis compared to existing healthcare systems.

Hybrid Mobile Cloud Computing Architecture with Load Balancing for Healthcare Systems

Healthcare is a fundamental part of every individual’s life. The healthcare industry is developing very rapidly with the help of advanced technologies. Many researchers are trying to build cloud-based healthcare applications that can be accessed by healthcare professionals from their premises, as well as by patients from their mobile devices through communication interfaces. These systems promote reliable and remote interactions between patients and healthcare professionals. However, there are several limitations to these innovative cloud computing-based systems, namely network availability, latency, battery life and resource availability. We propose a hybrid mobile cloud computing (HMCC) architecture to address these challenges. Furthermore, we also evaluate the performance of heuristic and dynamic machine learning based task scheduling and load balancing algorithms on our proposed architecture. We compare them, to identify the strengths and weaknesses of each algorithm; and provide their comparative results, to show latency and energy consumption performance. Challenging issues for cloud-based healthcare systems are discussed in detail.

Intelligent data deduplication with Deep Transfer Learning Enabled Classification Model for Cloud-based Healthcare System

The extensive adoption of cloud computing (CC) enabled healthcare systems in attaining medical data from different data sources sustained by heterogeneous cloud providers. Data deduplication (DD) is a proficient method to effectually share and store data in the cloud server. At the same time, deep learning (DL) models can be applied for effective decision-making in the cloud-based healthcare system. With this motivation, this study develops an Intelligent DD with Deep Transfer Learning Enabled Classification Model for Cloud-based Healthcare System (IDDTLC-CHS) model. The presented IDDTLC-CHS model aims to accomplish effective DD and classification in the cloud-enabled healthcare environment. For DD, the neighbourhood correlation sequence (NCS) algorithm is employed which generates optimum code words and is then compressed by Deflate model. Besides, the data classification module involves a series of processes namely fuzzy c-means (FCM) segmentation, Xception feature extraction; bidirectional gated recurrent unit (BiGRU), and seagull optimization algorithm (SGO). The SGO algorithm is applied for optimal adjustment of the parameters involved in the BiGRU model. To assess enhanced outcomes of the IDDTLC-CHS model, a wide-ranging simulation analysis is carried out using the benchmark dataset. The comparative analysis reported the betterment of the IDDTLC-CHS model compared to other recent approaches.

An enhanced encryption-based security framework in the CPS Cloud

The rapid advancement of computation techniques and cloud computing has led to substantial advancements in Cyber-Physical Systems (CPS), particularly in the field of health care. There are a variety of ways in which CPS is used in healthcare today, including delivering intelligent feedback systems, automatically updating patient data digitally, monitoring patients passively with biosensors, etc. In recent years, cyber-physical systems have become capable of making lifesaving decisions as they are becoming more connected to the cloud. However, healthcare has become one of the most critical issues for many. A CPS network uses the Internet of Medical Things (IoMT) to continuously monitor patients’ health metrics such as body temperature, heart rate, etc. Due to physical connectivity restrictions, networks are more susceptible to security threats. In spite of the fact that the data is stored in the cloud, it is necessary to provide security regardless of device security and network security. Several cyber-security vulnerabilities have been identified in cloud-based healthcare systems in particular. To give patients a reliable healthcare experience, security concerns with CPSs need to be addressed carefully. In this context, this paper proposes a Cross-Breed Blowfish and MD5 (CBM) approach to improve the security of health data in the CPS cloud. The proposed model uses the wireless sensor network, in which data acquired by the network is transmitted via the transmitting node. Using the fuzzified effective trust-based routing protocol (FET-RP), the most efficient path for data travel is selected. The best route is determined using Butter-Ant Optimization (BAO) algorithm. The proposed method conveys data throughput encryption and decryption in a decoded format. The encrypted data is then stored in the cloud database for security reasons. The route finding algorithm is the one which is sending the data from one end to other end. The data is encrypted based on the source and destination. We compare the performance metrics of our recommended technique to those of other existing techniques, such as RSA, Two fish, ICC, and FHEA, in order to ensure that it performs optimally. The values of Cross Breed Blowfish and MD5 and FET-RP with regard to the performance metrics in terms of encryption (60 ms), decryption (55 ms), latency (60 s), throughput (97 mbps), security level (98%), and execution time (57 ms) which outperforms the conventional methods by 10–15%. Also the proposed encryption shows the considerable improvement in the level of security making our model a real world solution.

Ethical Use of Web-based Welfare Technology for Caring Elderly People Who Live Alone in Korea: A Case Study

This study examined ethical ways to use welfare technology in a situation where the demand for non-face-to-face welfare services using Cloud based healthcare systems had increased rapidly in caring for elderly people who live alone. Through focus group interviews with social workers related to the care of elderly people who live alone, in-depth interviews were conducted on the current situation, problems, ethical issues, and development directions arising in the implementation of welfare technology. The main areas of interest were focused on improving safety in caring them using IoT technology and enhancing emotional support in preventing lonely deaths using companion robot and AI speaker. Issues such as the need for individualization, client-centeredness, privacy, self-determination, competence, informed consent, right to know, convenience, and advocacy were identified as important ethical considerations related to use of welfare technology. The research results suggested that various stakeholders should participate in the development of ethical indicators and welfare technology for the ethical use of welfare technology.

Morton Filter-Based Security Mechanism for Healthcare System in Cloud Computing.

Electronic health records contain the patient’s sensitive information. If these data are acquired by a malicious user, it will not only cause the pilferage of the patient’s personal data but also affect the diagnosis and treatment. One of the most challenging tasks in cloud-based healthcare systems is to provide security and privacy to electronic health records. Various probabilistic data structures and watermarking techniques were used in the cloud-based healthcare systems to secure patient’s data. Most of the existing studies focus on cuckoo and bloom filters, without considering their throughputs. In this research, a novel cloud security mechanism is introduced, which supersedes the shortcomings of existing approaches. The proposed solution enhances security with methods such as fragile watermark, least significant bit replacement watermarking, class reliability factor, and Morton filters included in the formation of the security mechanism. A Morton filter is an approximate set membership data structure (ASMDS) that proves many improvements to other data structures, such as cuckoo, bloom, semi-sorting cuckoo, and rank and select quotient filters. The Morton filter improves security; it supports insertions, deletions, and lookups operations and improves their respective throughputs by 0.9× to 15.5×, 1.3× to 1.6×, and 1.3× to 2.5×, when compared to cuckoo filters. We used Hadoop version 0.20.3, and the platform was Red Hat Enterprise Linux 6; we executed five experiments, and the average of the results has been taken. The results of the simulation work show that our proposed security mechanism provides an effective solution for secure data storage in cloud-based healthcare systems, with a load factor of 0.9. Furthermore, to aid cloud security in healthcare systems, we presented the motivation, objectives, related works, major research gaps, and materials and methods; we, thus, presented and implemented a cloud security mechanism, in the form of an algorithm and a set of results and conclusions.

A novel IoT-fog-cloud-based healthcare system for monitoring and predicting COVID-19 outspread.

Rapid communication of viral sicknesses is an arising public medical issue across the globe. Out of these, COVID-19 is viewed as the most critical and novel infection nowadays. The current investigation gives an effective framework for the monitoring and prediction of COVID-19 virus infection (C-19VI). To the best of our knowledge, no research work is focused on incorporating IoT technology for C-19 outspread over spatial–temporal patterns. Moreover, limited work has been done in the direction of prediction of C-19 in humans for controlling the spread of COVID-19. The proposed framework includes a four-level architecture for the expectation and avoidance of COVID-19 contamination. The presented model comprises COVID-19 Data Collection (C-19DC) level, COVID-19 Information Classification (C-19IC) level, COVID-19-Mining and Extraction (C-19ME) level, and COVID-19 Prediction and Decision Modeling (C-19PDM) level. Specifically, the presented model is used to empower a person/community to intermittently screen COVID-19 Fever Measure (C-19FM) and forecast it so that proactive measures are taken in advance. Additionally, for prescient purposes, the probabilistic examination of C-19VI is quantified as degree of membership, which is cumulatively characterized as a COVID-19 Fever Measure (C-19FM). Moreover, the prediction is realized utilizing the temporal recurrent neural network. Additionally, based on the self-organized mapping technique, the presence of C-19VI is determined over a geographical area. Simulation is performed over four challenging datasets. In contrast to other strategies, altogether improved outcomes in terms of classification efficiency, prediction viability, and reliability were registered for the introduced model.

Simultaneous Detection of Inflammatory Biomarkers by SERS Nanotag-Based Lateral Flow Assay with Portable Cloud Raman Spectrometer.

Inflammatory biomarkers are closely related to infectious diseases. However, traditional clinical tests of laboratory inspection are unable to achieve rapid and accurate detection of these biomarkers on-site due to shortcomings such as complex experimental operation, expensive equipment, and long test time. Herein, we proposed a lateral flow assay (LFA) strip based on surface-enhanced Raman scattering (SERS) nanotags (SERS-LFA strips) for the simultaneous and quantitative detection of dual infection biomarkers, serum amyloid A (SAA) and C-reactive protein (CRP), respectively. In practice, mesoporous silica (mSiO2)-coated Au nanoparticles (Au NPs) were used as the SERS substrate. Mercaptobenzoic acid (MBA) was embedded in the internal gap between Au NPs and the mSiO2 shell to prepare AuMBA@mSiO2 NPs, onto which SAA and CRP antibodies were modified to prepare two AuMBA@mSiO2 SERS nanotags. The Raman intensities of the test and control lines were simultaneously identified for the qualitative detection of SAA and CRP, with limits of detection (LODs) as low as 0.1 and 0.05 ng/mL for SAA and CRP, respectively. Finally, aiming at point-of-care testing (POCT) applications, we used a smartphone-based portable Raman spectrometer to quantitatively analyze the SERS-LFA strips. The Raman signal could still be accurately detected when the concentration of SAA and CRP was 10 ng/mL, which is lower than the LOD required in clinical practice for most diseases. Therefore, taking into account its simple operation and short analysis time, by using a portable Raman spectrometer which can be equipped with a 5G cloud-based healthcare management system, the current strategy based on SERS-LFA provides the potential for the quick and on-site diagnosis of infectious diseases such as sepsis, which is of great significance for medical guidance on the treatment of widely spread infection-related diseases in remote areas that lack well-developed medical resources.

Hybrid data encryption model integrating multi-objective adaptive genetic algorithm for secure medical data communication over cloud-based healthcare systems

The exponential rise in the development of cloud computing environments in the healthcare field, the protection and confidentiality of the medical records become a primary concern for healthcare services applications. Today, health data stored in the cloud is highly confidential information concealed to avoid unauthorized access to protect the patient’s information. As cloud-based medical data transmission becomes more common, it receives growing attention from researchers and academics. Despite the potential for misuse, medical data transmitted through unreliable networks can be manipulated or compromised. The current cryptosystems alone are not sufficient to deal with these issues, and hence this paper introduces a new hybridization of data encryption model to shelter the diagnosis data in medical images. The proposed model is developed by combining either 2D Discrete Wavelet Transform 1 Level (2D-DWT-1 L) or 2D Discrete Wavelet Transform 2 Level (2D-DWT-2 L) steganography with the proposed hybrid encryption scheme. The hybrid encryption scheme is built by strategically applying Advanced Encryption Standard (AES) and Rivest–Shamir–Adleman (RSA) algorithms to secure diagnosis data to be embedded with the RGB channels of medical cover image. One of the key novelties is the use of an Adaptive Genetic Algorithm for Optimal Pixel Adjustment Process (AGA-OPAP) that enriches data hiding ability as well as imperceptibility features. To evaluate the efficiency of the proposed model, numerical tests are performed. The results show that the proposed algorithm is capable of safely transmitting medical data. Comparison of results is carried out concerning the datasets with the state-of-the-art algorithm. In terms of various statistical measures, the results showed the superiority of the proposed algorithm, such as peak signal to noise ratio (PSNR), correlation, structural content (SC), structure similarity (SSIM), entropy, histogram, NPCR, UACI and embedding capacity. The proposed model can also prevent attacks, such as steganalysis or RS attacks.

LAPCHS: A lightweight authentication protocol for cloud-based health-care systems

The development of technology has accelerated things and solved many problems. Among various technologies, the integration of the Internet of Things (IoT) into human social life brings the promise of an easier and better life. The integration of IoT into the medical field, as well as the environments and patients associated with it, has provided a new context called the Telecare Medical Information Systems. It should be noted that the smart medicine, in addition to its significant benefits, carries many security threats, so privacy and especially anonymity is the largest concern in implementing a telecare medical information system.Due to the importance of privacy preservation and security in smart health-care systems, Fan et al. have recently proposed a lightweight authentication protocol (IEEE Netw. 33 (2) (2019)) using quadratic residue and pseudo random number generators, to be used in this platform. They believed that their scheme has enough security, privacy preservation, and also good resistance to various attacks including tag traceability attacks, replay attacks and also de-synchronization (DoS) attacks. The aim of the present study is to examine the security of the mentioned protocol. In fact, a heuristic attack was presented, in which an adversary could retrieve tag’s and reader’s current and previous identifier that contradicts anonymity and forward untraceability properties of this protocol. Reader and tag impersonation attacks were also applied against the protocol. Besides, a lightweight authentication protocol was proposed for cloud-based health-care systems called LAPCHS. Security analysis of our new protocol, through heuristic security analysis and also formal evaluations using Real-or-Random Model and simulations done on the AVISPA and the Scyther tools, confirms its security against different attacks.