eHealth Cloud Security Challenges: A Survey

Systematic review and meta-analysis for a Global Patient co-Owned Cloud (GPOC)

Cloud-based personal health records increase globally. The GPOC series introduces the concept of a Global Patient co-Owned Cloud (GPOC) of personal health records. Here, we present the GPOC series' Prospective Register of Systematic Reviews (PROSPERO) registered and Preferred Reporting Items Systematic and Meta-Analyses (PRISMA)-guided systematic review and meta-analysis. It examines cloud-based personal health records and factors such as data security, efficiency, privacy and cost-based measures. It is a meta-analysis of twelve relevant axes encompassing performance, cryptography and parameters based on efficiency (runtimes, key generation times), security (access policies, encryption, decryption) and cost (gas). This aims to generate a basis for further research, a GPOC sandbox model, and a possible construction of a global platform. This area lacks standard and shows marked heterogeneity. A consensus within this field would be beneficial to the development of a GPOC. A GPOC could spark the development and global dissemination of artificial intelligence in healthcare.

On-cloud decision-support system for non-small cell lung cancer histology characterization from thorax computed tomography scans

Non-Small Cell Lung Cancer (NSCLC) accounts for about 85% of all lung cancers. Developing non-invasive techniques for NSCLC histology characterization may not only help clinicians to make targeted therapeutic treatments but also prevent subjects from undergoing lung biopsy, which is challenging and could lead to clinical implications. The motivation behind the study presented here is to develop an advanced on-cloud decision-support system, named LUCY, for non-small cell LUng Cancer histologY characterization directly from thorax Computed Tomography (CT) scans. This aim was pursued by selecting thorax CT scans of 182 LUng ADenocarcinoma (LUAD) and 186 LUng Squamous Cell carcinoma (LUSC) subjects from four openly accessible data collections (NSCLC-Radiomics, NSCLC-Radiogenomics, NSCLC-Radiomics-Genomics and TCGA-LUAD), in addition to the implementation and comparison of two end-to-end neural networks (the core layer of whom is a convolutional long short-term memory layer), the performance evaluation on test dataset (NSCLC-Radiomics-Genomics) from a subject-level perspective in relation to NSCLC histological subtype location and grade, and the dynamic visual interpretation of the achieved results by producing and analyzing one heatmap video for each scan. LUCY reached test Area Under the receiver operating characteristic Curve (AUC) values above 77% in all NSCLC histological subtype location and grade groups, and a best AUC value of 97% on the entire dataset reserved for testing, proving high generalizability to heterogeneous data and robustness. Thus, LUCY is a clinically-useful decision-support system able to timely, non-invasively and reliably provide visually-understandable predictions on LUAD and LUSC subjects in relation to clinically-relevant information.

Ensuring privacy and confidentiality of cloud data: A comparative analysis of diverse cryptographic solutions based on run time trend

The cloud is becoming a hub for sensitive data as technology develops, making it increasingly vulnerable, especially as more people get access. Data should be protected and secured since a larger number of individuals utilize the cloud for a variety of purposes. Confidentiality and privacy of data is attained through the use of cryptographic techniques. While each cryptographic method completes the same objective, they all employ different amounts of CPU, memory, throughput, encryption, and decryption times. It is necessary to contrast the various possibilities in order to choose the optimal cryptographic algorithm. An integrated data size of 5n*102 (KB (∈ 1,2,4,10,20,40) is evaluated in this article. Performance metrics including run time, memory use, and throughput time were used in the comparison. To determine the effectiveness of each cryptographic technique, the data sizes were run fifteen (15) times, and the mean simulation results were then reported. In terms of run time trend, NCS is superior to the other algorithms according to Friedman's test and Bonferroni's Post Hoc test.

For-profit versus non-profit cybersecurity posture: breach types and locations in healthcare organisations

BACKGROUND

The implementation of emerging technologies has resulted in an increase of data breaches in healthcare organisations, especially during the COVID-19 pandemic. Health information and cybersecurity managers need to understand if, and to what extent, breach types and locations are associated with their organisation's business type.

OBJECTIVE

To investigate if breach type and breach location are associated with business type, and if so, investigate how these factors affect information systems and protected health information in for-profit versus non-profit organisations.

METHOD

The quantitative study was performed using chi-square tests for association and post-hoc comparison of column proportions analysis on an archival data set of reported healthcare data breaches from 2020 to 2022. Data from the Department of Health and Human Services website was retrieved and each organisation classified as for-profit or non-profit.

RESULTS

For-profit organisations experienced a significantly higher number of breaches due to theft, and non-profit organisations experienced a significantly higher number of breaches due to unauthorised access. Furthermore, the number of breaches that occurred on laptops and paper/films was significantly higher in for-profit organisations.

CONCLUSION

While the threat level of hacking techniques is the same in for-profit and non-profit organisations, certain breach types are more likely to occur within specific breach locations based on the organisation's business type. To protect the privacy and security of medical information, health information and cybersecurity managers need to align with industry-leading frameworks and controls to prevent specific breach types that occur in specific locations within their environments.

Privacy in electronic health records: a systematic mapping study

Main

Electronic health record (EHR) applications are digital versions of paper-based patient health information. Traditionally, medical records are made on paper. However, nowadays, advances in information and communication technology have made it possible to change medical records from paper to EHR. Therefore, preserving user data privacy is extremely important in healthcare environments. The main challenges are providing ways to make EHR systems increasingly capable of ensuring data privacy and at the same time not compromising the performance and interoperability of these systems.

Subject and methods

This systematic mapping study intends to investigate the current research on security and privacy requirements in EHR systems and identify potential research gaps in the literature. The main challenges are providing ways to make EHR systems increasingly capable of ensuring data privacy, and at the same time, not compromising the performance and interoperability of these systems. Our research was carried out in the Scopus database, the largest database of abstracts and citations in the literature with peer review.

Results

We have collected 848 articles related to the area. After disambiguation and filtering, we selected 30 articles for analysis. The result of such an analysis provides a comprehensive view of current research.

Conclusions

We can highlight some relevant research possibilities. First, we noticed a growing interest in privacy in EHR research in the last 6 years. Second, blockchain has been used in many EHR systems as a solution to achieve data privacy. However, it is a challenge to maintain traceability by recording metadata that can be mapped to private data of the users applying a particular mapping function that can be hosted outside the blockchain. Finally, the lack of a systematic approach between EHR solutions and existing laws or policies leads to better strategies for developing a certification process for EHR systems.

eHealth: A Survey of Architectures, Developments in mHealth, Security Concerns and Solutions

The ramifications of the COVID-19 pandemic have contributed in part to a recent upsurge in the study and development of eHealth systems. Although it is almost impossible to cover all aspects of eHealth in a single discussion, three critical areas have gained traction. These include the need for acceptable eHealth architectures, the development of mobile health (mHealth) technologies, and the need to address eHealth system security concerns. Existing survey articles lack a synthesis of the most recent advancements in the development of architectures, mHealth solutions, and innovative security measures, which are essential components of effective eHealth systems. Consequently, the present article aims at providing an encompassing survey of these three aspects towards the development of successful and efficient eHealth systems. Firstly, we discuss the most recent innovations in eHealth architectures, such as blockchain-, Internet of Things (IoT)-, and cloud-based architectures, focusing on their respective benefits and drawbacks while also providing an overview of how they might be implemented and used. Concerning mHealth and security, we focus on key developments in both areas while discussing other critical topics of importance for eHealth systems. We close with a discussion of the important research challenges and potential future directions as they pertain to architecture, mHealth, and security concerns. This survey gives a comprehensive overview, including the merits and limitations of several possible technologies for the development of eHealth systems. This endeavor offers researchers and developers a quick snapshot of the information necessary during the design and decision-making phases of the eHealth system development lifecycle. Furthermore, we conclude that building a unified architecture for eHealth systems would require combining several existing designs. It also points out that there are still a number of problems to be solved, so more research and investment are needed to develop and deploy functional eHealth systems.

A Comprehensive Study of Security and Cyber-Security Risk Management within e-Health Systems: Synthesis, Analysis and a Novel Quantified Approach

Internet of Things (IoT) applications are among the major trends of nowadays. Billions of connected devices are creating great business profits and performing a multitude of automated tasks in many daily human activities. In healthcare service delivery, IoT capabilities are difficult to overestimate, they are progressively becoming entangled and commonly coined Internet of Medical Things (IoMT). The participating nodes in IoMT networks generate, collect and exchange huge amounts of extremely private and sensitive data. Numerous security vulnerabilities arise due to the complexity and the heterogeneity of the technology. New risks, born out of IoMT systems, cannot easily be supported by existing risk management frameworks. The existing cyber-security risk assessment methods and approaches, deployed in several organizations, will not address the IoMT inherent risks properly. This study includes a comprehensive review of IoMT systems. Popular risk assessment methods are discussed and their suitability to IoMT is dealt with in detail. Based on this study, we propose a framework to enhance trust and help with decision making in e-healthcare environments given its high-risk exposure. The proposal is based on a quantified risk assessment approach. Our aim is to define a novel approach/model for improving trust and risk management in an e-health context.

Cloud Based AI-Driven Video Analytics (CAVs) in Laparoscopic Surgery: A Step Closer to a Virtual Portfolio

Aims: To outline the use of cloud-based artificial intelligence (AI)-driven video analytics (CAVs) in minimally invasive surgery and to propose their potential as a virtual portfolio for trainee and established surgeons. 

Methods: An independent online demonstration was requested from three platforms, namely Theator (Palo Alto, California, USA), Touch Surgery™ (Medtronic, London, England, UK), and C-SATS® (Seattle, Washington, USA). The assessed domains were online and app-based accessibility, the ability for timely trainee feedback, and AI integration for operation-specific steps and critical views.

Results: The CAVs enable users to record surgeries with the advantage of limitless video storage through clouding and smart integration into theatre settings. This can be used to view surgeries and review trainee videos through a medium of communication and sharing with the ability to provide feedback. Theator and C-SATS® provide their users with surgical skills scoring systems with customizable options that can be used to provide structured feedback to trainees. Additionally, AI plays an important role in all three platforms by providing time-based analysis of steps and highlighting critical milestones. 

Conclusion: Cloud-based AI-driven video analytics is an emerging new technology that enables users to store, analyze, and review videos. This technology has the potential to improve training, governance, and standardization procedures. Moreover, with the future adaptation of the technology, CAVs can be integrated into the trainees’ portfolios as part of their virtual curriculum. This can enable a structured assessment of a surgeon’s progression and degree of experience throughout their surgical career.

Analytical demands to use whole-genome sequencing in precision oncology

Interrogating the tumor genome in its entirety by whole-genome sequencing (WGS) offers an unprecedented insight into the biology and pathogenesis of cancer, with potential impact on diagnostics, prognostication and therapy selection. WGS is able to detect sequence as well as structural variants and thereby combines central domains of cytogenetics and molecular genetics. Given the potential of WGS in directing targeted therapeutics and clinical decision-making, we envision a gradual transition of the method from research to clinical routine. This review is one out of three within this issue aimed at facilitating this effort, by discussing in-depth analytical validation, clinical interpretation and clinical utility of WGS. The review highlights the requirements for implementing, validating and maintaining a clinical WGS pipeline to obtain high-quality patient-specific data in accordance with the local regulatory landscape. Every step of the WGS pipeline, which includes DNA extraction, library preparation, sequencing, bioinformatics analysis, and data storage, is considered with respect to its logistics, necessities, potential pitfalls, and the required quality management. WGS is likely to drive clinical diagnostics and patient care forward, if requirements and challenges of the technique are recognized and met.

A novel hybrid-based approach of snort automatic rule generator and security event correlation (SARG-SEC)

The rapid advanced technological development alongside the Internet with its cutting-edge applications has positively impacted human society in many aspects. Nevertheless, it equally comes with the escalating privacy and critical cybersecurity concerns that can lead to catastrophic consequences, such as overwhelming the current network security frameworks. Consequently, both the industry and academia have been tirelessly harnessing various approaches to design, implement and deploy intrusion detection systems (IDSs) with event correlation frameworks to help mitigate some of these contemporary challenges. There are two common types of IDS: signature and anomaly-based IDS. Signature-based IDS, specifically, Snort works on the concepts of rules. However, the conventional way of creating Snort rules can be very costly and error-prone. Also, the massively generated alerts from heterogeneous anomaly-based IDSs is a significant research challenge yet to be addressed. Therefore, this paper proposed a novel Snort Automatic Rule Generator (SARG) that exploits the network packet contents to automatically generate efficient and reliable Snort rules with less human intervention. Furthermore, we evaluated the effectiveness and reliability of the generated Snort rules, which produced promising results. In addition, this paper proposed a novel Security Event Correlator (SEC) that effectively accepts raw events (alerts) without prior knowledge and produces a much more manageable set of alerts for easy analysis and interpretation. As a result, alleviating the massive false alarm rate (FAR) challenges of existing IDSs. Lastly, we have performed a series of experiments to test the proposed systems. It is evident from the experimental results that SARG-SEC has demonstrated impressive performance and could significantly mitigate the existing challenges of dealing with the vast generated alerts and the labor-intensive creation of Snort rules.

Znet: Deep Learning Approach for 2D MRI Brain Tumor Segmentation

Background: Detection and segmentation of brain tumors using MR images are challenging and valuable tasks in the medical field. Early diagnosing and localizing of brain tumors can save lives and provide timely options for physicians to select efficient treatment plans. Deep learning approaches have attracted researchers in medical imaging due to their capacity, performance, and potential to assist in accurate diagnosis, prognosis, and medical treatment technologies. Methods and procedures: This paper presents a novel framework for segmenting 2D brain tumors in MR images using deep neural networks (DNN) and utilizing data augmentation strategies. The proposed approach (Znet) is based on the idea of skip-connection, encoder-decoder architectures, and data amplification to propagate the intrinsic affinities of a relatively smaller number of expert delineated tumors, e.g., hundreds of patients of the low-grade glioma (LGG), to many thousands of synthetic cases. Results: Our experimental results showed high values of the mean dice similarity coefficient (dice = 0.96 during model training and dice = 0.92 for the independent testing dataset). Other evaluation measures were also relatively high, e.g., pixel accuracy = 0.996, F1 score = 0.81, and Matthews Correlation Coefficient, MCC = 0.81. The results and visualization of the DNN-derived tumor masks in the testing dataset showcase the ZNet model’s capability to localize and auto-segment brain tumors in MR images. This approach can further be generalized to 3D brain volumes, other pathologies, and a wide range of image modalities. Conclusion: We can confirm the ability of deep learning methods and the proposed Znet framework to detect and segment tumors in MR images. Furthermore, pixel accuracy evaluation may not be a suitable evaluation measure for semantic segmentation in case of class imbalance in MR images segmentation. This is because the dominant class in ground truth images is the background. Therefore, a high value of pixel accuracy can be misleading in some computer vision applications. On the other hand, alternative evaluation metrics, such as dice and IoU (Intersection over Union), are more factual for semantic segmentation. Clinical impact: Artificial intelligence (AI) applications in medicine are advancing swiftly, however, there is a lack of deployed techniques in clinical practice. This research demonstrates a practical example of AI applications in medical imaging, which can be deployed as a tool for auto-segmentation of tumors in MR images.

Nurses' experiences of using AsthmaTuner - an eHealth self-management system for healthcare of patients with asthma

Objective

This study describes nurses' experiences of using eHealth for assessment in the healthcare of patients with asthma.

Methods

Five nurses with experience of using AsthmaTuner in the healthcare of patients with asthma participated in the study. Individual semi-structured interviews were conducted with the nurses to understand their experiences of using the eHealth system. The transcribed interviews were analyzed using qualitative content analysis.

Results

The results show that nurses as well as patients find the tool useful and easy-to-handle. AsthmaTuner gives the nurses access to more and better information about the patients, which facilitates assessments and makes their work more efficient. The patients become more involved in their care, gain increased control and take more responsibility for their illness and treatment.

Conclusions

The nurses appreciate eHealth in asthma care. Using AsthmaTuner makes the nurses' work more efficient and the patients become more involved in their care.

Current and Future Photography Techniques in Aesthetic Surgery

Background

The rapidly increasing modalities and mediums of clinical photography, use of 3-dimensional (3D) and 4-dimensional (4D) patient modeling, and widening implementation of cloud-based storage and artificial intelligence (AI) call for an overview of various methods currently in use as well as future considerations in the field.

Objectives

Through a close look at the methods used in aesthetic surgery photography, clinicians will be able to select the modality best suited to their practice and goals.

Methods

Review and discussion of current data pertaining to: 2-dimensional (2D) and 3D clinical photography, current photography software, augmented reality reconstruction, AI photography, and cloud-based storage.

Results

Important considerations for current image capture include a device with a gridded viewing screen and high megapixel resolution, a tripod with leveling base, studio lighting with dual-sourced light, standardized matte finish background, and consistency in patient orientation. Currently, 3D and 4D photography devices offer advantages such as improved communication to the patient on outcome expectation and better quality of patient service and safety. AI may contribute to post-capture processing and 3D printing of postoperative outcomes. Current smartphones distort patient perceptions about their appearance and should be used cautiously in an aesthetic surgery setting. Cloud-based storage provides flexibility, cost, and ease of service while remaining vulnerable to data breaches.

Conclusions

While there are advancements to be made in the physical equipment and preparation for the photograph, the future of clinical photography will be heavily influenced by innovations in software and 3D and 4D modeling of outcomes.

Security challenges and solutions using healthcare cloud computing

Cloud computing is among the most beneficial solutions to digital problems. Security is one of the focal issues in cloud computing technology, and this study aims at investigating security issues of cloud computing and their probable solutions. A systematic review was performed using Scopus, Pubmed, Science Direct, and Web of Science databases. Once the title and abstract were evaluated, the quality of studies was assessed in order to choose the most relevant according to exclusion and inclusion criteria. Then, the full texts of studies selected were read thoroughly to extract the necessary results. According to the review, data security, availability, and integrity, as well as information confidentiality and network security, were the major challenges in cloud security. Further, data encryption, authentication, and classification, besides application programming interfaces (API), were security solutions to cloud infrastructure. Data encryption could be applied to store and retrieve data from the cloud in order to provide secure communication. Besides, several central challenges, which make the cloud security engineering process problematic, have been considered in this study.

A Study on the Relationship between Usability of GUIs and Power Consumption of a PC: The Case of PHRs

Usability is key to achieve quality in software products. The client applications with a high score in usability might impact on the power consumption when they are run in a PC. For this reason, energy savings turn to be critical in green software systems. In this paper the relationship between the usability evaluations of the GUIs and the power consumption measurements of the main components of a PC were analysed. A set of 5 web-based personal health records (PHRs) were selected as a case study. The usability assessment was performed by an expert, employing the 14 principles of design by Alan Dix as heuristics. They were scored on a Likert scale after performing a collection of common tasks in the PHRs. At the same time, an equipment to measure the energy consumption of hard disk drive, graphics card, processor, monitor and power supply was used. Spearman’s index was studied for the correlations between the usability assessments and the power consumption measurements. As a results, some weak relationships were found. A total of 5 usability heuristics were observed to may influence energy consumption when they were considered in the implementation of the PHRs. These heuristics were the following ones: consistency, task migratability, observability, recoverability and responsiveness. Based on the results, the usability principles of design cannot always be related to lower energy consumption. Future research should focus on the tradeoffs between usability and power consumption of client applications when they are used in a computer.

A novel approach to data integrity auditing in PCS: Minimising any Trust on Third Parties (DIA-MTTP)

Data Integrity Auditing (DIA) is a security service for verifying the integrity of outsourced data in Public Cloud Storage (PCS) by users or by Third-Party Auditors (TPAs) on behalf of the users. This paper proposes a novel DIA framework, called DIA-MTTP. The major novelty of the framework lies in that, while providing the DIA service in a PCS environment, it supports the use of third parties, but does not require full trust in the third parties. In achieving this property, a number of ideas also have been embedded in the design. These ideas include the use of multiple third parties and a hierarchical approach to their communication structure making the service more suited to resource-constrained user devices, the provision of two integrity assurance levels to balance the trade-off between security protection levels and the costs incurred, the application of a data deduplication measure to both new data and existing data updates to minimise the number of tags (re-)generated. In supporting the dynamic data and deduplication measure, a distributed data structure, called Multiple Mapping Tables (M2T), is proposed. Security analysis indicates that our framework is secure with the use of untrusted third parties. Performance evaluation indicates that our framework imposes less computational, communication and storage overheads than related works.

ReportFlow: an application for EEG visualization and reporting using cloud platform

Background

The cloud is a promising resource for data sharing and computing. It can optimize several legacy processes involving different units of a company or more companies. Recently, cloud technology applications are spreading out in the healthcare setting as well, allowing to cut down costs for physical infrastructures and staff movements. In a public environment the main challenge is to guarantee the patients’ data protection. We describe a cloud-based system, named ReportFlow, developed with the aim to improve the process of reporting and delivering electroencephalograms.

Methods

We illustrate the functioning of this application through a use-case scenario occurring in an Italian hospital, and describe the corresponding key encryption and key management used for data security guarantee. We used the X² test or the unpaired Student t test to perform pre-post comparisons of some indexes, in order to evaluate significant changes after the application of ReportFlow.

Results

The results obtained through the use of ReportFlow show a reduction of the time for exam reporting (t = 19.94; p < 0.001) and for its delivering (t = 14.95; p < 0.001), as well as an increase of the number of neurophysiologic examinations performed (about 20%), guaranteeing data integrity and security. Moreover, 68% of exam reports were delivered completely digitally.

Conclusions

The application resulted to be an optimal solution to optimize the legacy process adopted in this scenario. The comparative pre-post analysis showed promising preliminary results of performance. Future directions will be the creation and release of certificates automatically.

A Lightweight Internet Sharing Scheme for Sectional Medical Images according to Existing Hospital Network Facilities and Basic Information Security Rules

Background

With the outbreak of COVID-19, large-scale telemedicine applications can play an important role in the epidemic areas or less developed areas. However, the transmission of hundreds of megabytes of Sectional Medical Images (SMIs) from hospital's Intranet to the Internet has the problems of efficiency, cost, and security. This article proposes a novel lightweight sharing scheme for permitting Internet users to quickly and safely access the SMIs from a hospital using an Internet computer anywhere but without relying on a virtual private network or another complex deployment.

Methods

A four-level endpoint network penetration scheme based on the existing hospital network facilities and information security rules was proposed to realize the secure and lightweight sharing of SMIs over the Internet. A "Master-Slave" interaction to the interactive characteristics of multiplanar reconstruction and maximum/minimum/average intensity projection was designed to enhance the user experience. Finally, a prototype system was established.

Results

When accessing SMIs with a data size ranging from 251.6 to 307.04 MB with 200 kBps client bandwidth (extreme test), the network response time to each interactive request remained at approximately 1 s, the original SMIs were kept in the hospital, and the deployment did not require a complex process; the imaging quality and interactive experience were recognized by radiologists.

Conclusions

This solution could serve Internet medicine at a low cost and may promote the diversified development of mobile medical technology. Under the current COVID-19 epidemic situation, we expect that it could play a low-cost and high-efficiency role in remote emergency support.

Precision health data: Requirements, challenges and existing techniques for data security and privacy

Precision health leverages information from various sources, including omics, lifestyle, environment, social media, medical records, and medical insurance claims to enable personalized care, prevent and predict illness, and precise treatments. It extensively uses sensing technologies (e.g., electronic health monitoring devices), computations (e.g., machine learning), and communication (e.g., interaction between the health data centers). As health data contain sensitive private information, including the identity of patient and carer and medical conditions of the patient, proper care is required at all times. Leakage of these private information affects the personal life, including bullying, high insurance premium, and loss of job due to the medical history. Thus, the security, privacy of and trust on the information are of utmost importance. Moreover, government legislation and ethics committees demand the security and privacy of healthcare data. Besides, the public, who is the data source, always expects the security, privacy, and trust of their data. Otherwise, they can avoid contributing their data to the precision health system. Consequently, as the public is the targeted beneficiary of the system, the effectiveness of precision health diminishes. Herein, in the light of precision health data security, privacy, ethical and regulatory requirements, finding the best methods and techniques for the utilization of the health data, and thus precision health is essential. In this regard, firstly, this paper explores the regulations, ethical guidelines around the world, and domain-specific needs. Then it presents the requirements and investigates the associated challenges. Secondly, this paper investigates secure and privacy-preserving machine learning methods suitable for the computation of precision health data along with their usage in relevant health projects. Finally, it illustrates the best available techniques for precision health data security and privacy with a conceptual system model that enables compliance, ethics clearance, consent management, medical innovations, and developments in the health domain.

Security and Privacy Requirements for Cloud Computing in Healthcare

Cloud computing promises essential improvements in healthcare delivery performance. However, its wide adoption in healthcare is yet to be seen, one main reason being patients’ concerns for security and privacy of their sensitive medical records. These concerns can be addressed through corresponding security and privacy requirements within the system engineering process. Despite a plethora of related research, security and privacy requirements for cloud systems and services have seldomly been investigated methodically so far, whereas their individual priorities to increase the system success probability have been neglected. Against this background, this study applies a systematic requirements engineering process: First, based on a systematic literature review, an extensive initial set of security and privacy requirements is elicited. Second, an online survey based on the best-worst scaling method is designed, conducted, and evaluated to determine priorities of security and privacy requirements.

Our results show that confidentiality and integrity of medical data are ranked at the top of the hierarchy of prioritized requirements, followed by control of data use and modification, patients’ anonymity, and patients’ control of access rights. Availability, fine-grained access control, revocation of access rights, flexible access, clinicians’ anonymity, as well as usability, scalability, and efficiency of the system complete the ranking. The level of agreement among patients is rather small, but statistically significant at the 0.01 level.

The main contribution of the present research comprises the study method and results highlighting the role of strong security and privacy and excluding any trade-offs with system usability. Enabling a richer understanding of patients’ security and privacy requirements for adopting cloud computing in healthcare, these are of particular importance to researchers and practitioners interested in supporting the process of security and privacy engineering for health-cloud solutions. It further represents a supplement that can support time-intensive negotiation meetings between the requirements engineers and patients.