Healthcare SaaS Based on a Data Model with Built-In Security and Privacy

2016 ◽  
Vol 6 (3) ◽  
pp. 1-14 ◽  
Author(s):  
Ruchika Asija ◽  
Rajarathnam Nallusamy
Keyword(s):  
Cloud Computing ◽  
Performance Evaluation ◽  
Data Model ◽  
Health Data ◽  
Software As A Service ◽  
Security And Privacy ◽  
Healthcare Industry ◽  
Access Controls ◽  
Software And Hardware ◽  
Security Levels

Cloud computing is a major technology enabler for providing efficient services at affordable costs by reducing the costs of traditional software and hardware licensing models. As it continues to evolve, it is widely being adopted by healthcare organisations. But hosting healthcare solutions on cloud is challenging in terms of security and privacy of health data. To address these challenges and to provide security and privacy to health data on the cloud, the authors present a Software-as-a-Service (SaaS) application with a data model with built-in security and privacy. This data model enhances security and privacy of the data by attaching security levels in the data itself expressed in the form of XML instead of relying entirely on application level access controls. They also present the performance evaluation of their application using this data model with different scaling indicators. To further investigate the adoption of IT and cloud computing in Indian healthcare industry they have done a survey of some major hospitals in India.

Healthcare SaaS Based on a Data Model With Built-In Security and Privacy

2019 ◽  
pp. 744-759 ◽  
Author(s):  
Ruchika Asija ◽  
Rajarathnam Nallusamy
Keyword(s):  
Cloud Computing ◽  
Performance Evaluation ◽  
Data Model ◽  
Health Data ◽  
Software As A Service ◽  
Security And Privacy ◽  
Healthcare Industry ◽  
Access Controls ◽  
Software And Hardware ◽  
Security Levels

Cloud computing is a major technology enabler for providing efficient services at affordable costs by reducing the costs of traditional software and hardware licensing models. As it continues to evolve, it is widely being adopted by healthcare organisations. But hosting healthcare solutions on cloud is challenging in terms of security and privacy of health data. To address these challenges and to provide security and privacy to health data on the cloud, the authors present a Software-as-a-Service (SaaS) application with a data model with built-in security and privacy. This data model enhances security and privacy of the data by attaching security levels in the data itself expressed in the form of XML instead of relying entirely on application level access controls. They also present the performance evaluation of their application using this data model with different scaling indicators. To further investigate the adoption of IT and cloud computing in Indian healthcare industry they have done a survey of some major hospitals in India.

Cloud Computing for Cytopathologists

2014 ◽  
pp. 250-271 ◽  
Author(s):  
Abraham Pouliakis ◽  
Stavros Archondakis ◽  
Efrossyni Karakitsou ◽  
Petros Karakitsos
Keyword(s):  
Cloud Computing ◽  
Computer Software ◽  
Security And Privacy ◽  
Software Systems ◽  
System Architectures ◽  
Use Of Resources ◽  
Processing Power ◽  
Software And Hardware ◽  
Cloud Applications ◽  
Near Future

Cloud computing is changing the way enterprises, institutions, and people understand, perceive, and use current software systems. Cloud computing is an innovative concept of creating a computer grid using the Internet facilities aiming at the shared use of resources such as computer software and hardware. Cloud-based system architectures provide many advantages in terms of scalability, maintainability, and massive data processing. By means of cloud computing technology, cytopathologists can efficiently manage imaging units by using the latest software and hardware available without having to pay for it at non-affordable prices. Cloud computing systems used by cytopathology departments can function on public, private, hybrid, or community models. Using cloud applications, infrastructure, storage services, and processing power, cytopathology laboratories can avoid huge spending on maintenance of costly applications and on image storage and sharing. Cloud computing allows imaging flexibility and may be used for creating a virtual mobile office. Security and privacy issues have to be addressed in order to ensure Cloud computing wide implementation in the near future. Nowadays, cloud computing is not widely used for the various tasks related to cytopathology; however, there are numerous fields for which it can be applied. The envisioned advantages for the everyday practice in laboratories' workflow and eventually for the patients are significant. This is explored in this chapter.

scholarly journals eHealth Cloud Security Challenges: A Survey

2019 ◽  
Vol 2019 ◽  
pp. 1-15 ◽  
Author(s):  
Yazan Al-Issa ◽  
Mohammad Ashraf Ottom ◽  
Ahmed Tamrawi
Keyword(s):  
Cloud Computing ◽  
Resource Sharing ◽  
Energy Savings ◽  
Service Providers ◽  
Healthcare Providers ◽  
Cloud Service ◽  
Cloud Security ◽  
Security And Privacy ◽  
Healthcare Industry ◽  
Sensitive Data

Cloud computing is a promising technology that is expected to transform the healthcare industry. Cloud computing has many benefits like flexibility, cost and energy savings, resource sharing, and fast deployment. In this paper, we study the use of cloud computing in the healthcare industry and different cloud security and privacy challenges. The centralization of data on the cloud raises many security and privacy concerns for individuals and healthcare providers. This centralization of data (1) provides attackers with one-stop honey-pot to steal data and intercept data in-motion and (2) moves data ownership to the cloud service providers; therefore, the individuals and healthcare providers lose control over sensitive data. As a result, security, privacy, efficiency, and scalability concerns are hindering the wide adoption of the cloud technology. In this work, we found that the state-of-the art solutions address only a subset of those concerns. Thus, there is an immediate need for a holistic solution that balances all the contradicting requirements.

scholarly journals Performance Evaluation of SUMEGHA Cloud Computing Environment: Methodologies & Tool

2019 ◽  
Vol 8 (12) ◽  
pp. 875-880
Keyword(s):  
Cloud Computing ◽  
Performance Evaluation ◽  
Software As A Service ◽  
Automated Testing ◽  
Cloud Environment ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Platform As A Service ◽  
Testing Tools ◽  
Manual Testing

Cloud ecosystem basically offers Platform as a Service (PaaS), Infrastructure as a Service (IaaS) and Software as a Service (SaaS). This paper describes the testing process employed for testing the C-DAC cloud SuMegha. Though new tools for the testing cloud are emerging into the market, there are aspects which are suited for manual testing and some which can be speeded up using automated testing tools. This paper brings out the techniques best suited to test different features of Cloud computing environment. It offers a comparison of several tools which enhance the testing process at each level. The authors also try to bring out (recommend) broad guidelines to follow while setting up a cloud environment to reduce the number of bugs in the system

scholarly journals Adopting cloud computing within the healthcare industry: opportunity or risk?

2016 ◽  
Vol 4 (1) ◽  
pp. 1-16 ◽  
Author(s):  
Linda Barthelus
Keyword(s):  
Cloud Computing ◽  
Decision Process ◽  
Business Environment ◽  
Security And Privacy ◽  
Innovative Technology ◽  
Healthcare Industry ◽  
Cloud Adoption ◽  
Innovation Research ◽  
Privacy Risks ◽  
Analytical Tools

Innovative technologies enable firms to strengthen their market position in today’s increasingly turbulent and competitive business environment. Cloud computing, an innovative technology, allows users to process and store data virtually via the internet and central remote servers. The purpose of this paper is to examine the forces that influence the adoption of cloud computing within the healthcare industry, through the theoretical lens of innovation resistance and the innovation decision process. This paper applied an evidence-based research methodology that consists of a systematic review of primary literature and a thematic synthesis of findings. The findings indicate that the primary reasons for resistance to cloud adoption within the healthcare industry are security and privacy risks to sensitive patient data, integration challenges, and a firms’ potential to lose control of data to cloud providers. However, incorporating analytical tools and safeguards into the decision process can mitigate these challenges. This study deepens knowledge of innovation resistance, which has been limited to innovation research thus far, and presents a conceptual model of how resistance affects each stage of the innovation decision process. This study proposes the cloud adoption toolkit to healthcare decision makers as a practical solution to address the challenges of cloud adoption.

scholarly journals Proposal and Assessment of a De-Identification Strategy to Enhance Anonymity of the Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM) in a Public Cloud-Computing Environment: Anonymization of Medical Data Using Privacy Models (Preprint)

2020 ◽  
Author(s):  
Seungho Jeon ◽  
Jeongeun Seo ◽  
Sukyoung Kim ◽  
Jeongmoon Lee ◽  
Jong-Ho Kim ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Data Model ◽  
Personal Information ◽  
Health Data ◽  
Common Data Model ◽  
Patient Privacy ◽  
Medical Outcomes ◽  
Cloud Computing System ◽  
Privacy Models ◽  
Identification Strategy

BACKGROUND De-identifying personal information is critical when using personal health data for secondary research. The Observational Medical Outcomes Partnership Common Data Model (CDM), defined by the nonprofit organization Observational Health Data Sciences and Informatics, has been gaining attention for its use in the analysis of patient-level clinical data obtained from various medical institutions. When analyzing such data in a public environment such as a cloud-computing system, an appropriate de-identification strategy is required to protect patient privacy. OBJECTIVE This study proposes and evaluates a de-identification strategy that is comprised of several rules along with privacy models such as k-anonymity, l-diversity, and t-closeness. The proposed strategy was evaluated using the actual CDM database. METHODS The CDM database used in this study was constructed by the Anam Hospital of Korea University. Analysis and evaluation were performed using the ARX anonymizing framework in combination with the k-anonymity, l-diversity, and t-closeness privacy models. RESULTS The CDM database, which was constructed according to the rules established by Observational Health Data Sciences and Informatics, exhibited a low risk of re-identification: The highest re-identifiable record rate (11.3%) in the dataset was exhibited by the DRUG_EXPOSURE table, with a re-identification success rate of 0.03%. However, because all tables include at least one “highest risk” value of 100%, suitable anonymizing techniques are required; moreover, the CDM database preserves the “source values” (raw data), a combination of which could increase the risk of re-identification. Therefore, this study proposes an enhanced strategy to de-identify the source values to significantly reduce not only the highest risk in the k-anonymity, l-diversity, and t-closeness privacy models but also the overall possibility of re-identification. CONCLUSIONS Our proposed de-identification strategy effectively enhanced the privacy of the CDM database, thereby encouraging clinical research involving multiple centers.

scholarly journals Security of cloud computing: belongings for the generations

2020 ◽  
Vol 9 (2) ◽  
pp. 454
Author(s):  
Jahangir Jabbar ◽  
Hussain Mehmood ◽  
Hassaan Malik
Keyword(s):  
Cloud Computing ◽  
Industrial Revolution ◽  
Software As A Service ◽  
Security And Privacy ◽  
It Management ◽  
Privacy Risk ◽  
Platform As A Service ◽  
Fourth Industrial Revolution ◽  
The Future ◽  
Reduced Costs

Cloud computing plays an important role in Information Technology (IT) management. Consequently, various cloud computing developers and users experience different benefits and similarly challenges to its use and potential opportunities in driving the Fourth Industrial Revolution. Despite the increasing benefits of cloud computing, including increased speed of data processing and reduced costs compared to traditional computing, issues of security and privacy risk remain one of the greatest concerns in cloud computing. Through a systematic literature review, the evolution and developments in technology and issues of security and privacy from many years are examined to establish the trends in the threats; and thereby, provides a projection on the future of security of cloud computing. This paper presents the cloud computing aspects (types/aspects, and categories such as Infrastructure as a Service (IaaS), Platform as a Service (PaaS) and Software as a Service (SaaS). The paper dwells on the challenges of cloud computing going into the future with some solutions that have potential to work.   

scholarly journals Proposal and Assessment of a De-Identification Strategy to Enhance Anonymity of the Observational Medical Outcomes Partnership Common Data Model (OMOP-CDM) in a Public Cloud-Computing Environment: Anonymization of Medical Data Using Privacy Models

10.2196/19597 ◽  
2020 ◽  
Vol 22 (11) ◽  
pp. e19597
Author(s):  
Seungho Jeon ◽  
Jeongeun Seo ◽  
Sukyoung Kim ◽  
Jeongmoon Lee ◽  
Jong-Ho Kim ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Data Model ◽  
Personal Information ◽  
Health Data ◽  
Common Data Model ◽  
Patient Privacy ◽  
Medical Outcomes ◽  
Cloud Computing System ◽  
Privacy Models ◽  
Identification Strategy

Background De-identifying personal information is critical when using personal health data for secondary research. The Observational Medical Outcomes Partnership Common Data Model (CDM), defined by the nonprofit organization Observational Health Data Sciences and Informatics, has been gaining attention for its use in the analysis of patient-level clinical data obtained from various medical institutions. When analyzing such data in a public environment such as a cloud-computing system, an appropriate de-identification strategy is required to protect patient privacy. Objective This study proposes and evaluates a de-identification strategy that is comprised of several rules along with privacy models such as k-anonymity, l-diversity, and t-closeness. The proposed strategy was evaluated using the actual CDM database. Methods The CDM database used in this study was constructed by the Anam Hospital of Korea University. Analysis and evaluation were performed using the ARX anonymizing framework in combination with the k-anonymity, l-diversity, and t-closeness privacy models. Results The CDM database, which was constructed according to the rules established by Observational Health Data Sciences and Informatics, exhibited a low risk of re-identification: The highest re-identifiable record rate (11.3%) in the dataset was exhibited by the DRUG_EXPOSURE table, with a re-identification success rate of 0.03%. However, because all tables include at least one “highest risk” value of 100%, suitable anonymizing techniques are required; moreover, the CDM database preserves the “source values” (raw data), a combination of which could increase the risk of re-identification. Therefore, this study proposes an enhanced strategy to de-identify the source values to significantly reduce not only the highest risk in the k-anonymity, l-diversity, and t-closeness privacy models but also the overall possibility of re-identification. Conclusions Our proposed de-identification strategy effectively enhanced the privacy of the CDM database, thereby encouraging clinical research involving multiple centers.

Cloud Computing for Cytopathologists

2015 ◽  
pp. 1312-1332
Author(s):  
Abraham Pouliakis ◽  
Stavros Archondakis ◽  
Efrossyni Karakitsou ◽  
Petros Karakitsos
Keyword(s):  
Cloud Computing ◽  
Computer Software ◽  
Security And Privacy ◽  
Software Systems ◽  
System Architectures ◽  
Use Of Resources ◽  
Processing Power ◽  
Software And Hardware ◽  
Cloud Applications ◽  
Near Future

Cloud computing is changing the way enterprises, institutions, and people understand, perceive, and use current software systems. Cloud computing is an innovative concept of creating a computer grid using the Internet facilities aiming at the shared use of resources such as computer software and hardware. Cloud-based system architectures provide many advantages in terms of scalability, maintainability, and massive data processing. By means of cloud computing technology, cytopathologists can efficiently manage imaging units by using the latest software and hardware available without having to pay for it at non-affordable prices. Cloud computing systems used by cytopathology departments can function on public, private, hybrid, or community models. Using cloud applications, infrastructure, storage services, and processing power, cytopathology laboratories can avoid huge spending on maintenance of costly applications and on image storage and sharing. Cloud computing allows imaging flexibility and may be used for creating a virtual mobile office. Security and privacy issues have to be addressed in order to ensure Cloud computing wide implementation in the near future. Nowadays, cloud computing is not widely used for the various tasks related to cytopathology; however, there are numerous fields for which it can be applied. The envisioned advantages for the everyday practice in laboratories' workflow and eventually for the patients are significant. This is explored in this chapter.

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