A Survey on Cloud Computing Architectures for Healthcare

Transcription

1 A Survey on Cloud Computing Architectures for Healthcare Pragnya Balamurukesan Department of Computer Engineering Dwarkadas J.Sanghvi College of Engineering, Mumbai,India Dr. Narendra M. Shekokar Head, Department of Computer Engineering Dwarkadas J.Sanghvi College of Engineering, Mumbai,India ABSTRACT We have analyzed several cloud computing architectures with increasing complexity. In particular we have reviewed those architectures that are applied in the healthcare domain. The size of patient records and other healthcare data is very large. Therefore storage and sharing of this data is complex. In addition to this healthcare data needs to be quickly retrievable and shareable. Security challenges must also be faced. In order to overcome these issues cloud computing is used. Cloud computing improves speed and ubiquity of access and sharing, security, mobility and allows analysis of the data. The first model we consider is a traditional cloud model, the second is a hybrid model which provides optimal allocation of resources and the third is a hybrid model developed for maximum privacy. We have compared three such architectures on the basis of their objectives, components, performance, types of cloud used, advantages, disadvantages and security parameters. Keywords Cloud computing, healthcare, health monitoring, privacy protection INTRODUCTION In an emergency situation the diagnosis and treatment prescribed by the doctor would be quicker and more accurate if he had access to previous medical records of the patient. Clinical data like allergies or a cardiac condition play a pivotal role in diagnosis and treatment. Before the digitalization of medical records, each patients file had to be manually updated and monitored. The next step was to store these clinical records on the hospitals computer systems. However, in case patient records had to be accessed by another doctor at another hospital the procedure was slow and complicated. Thus cloud storage was the next logical choice. Cloud computing is cheaper than manual systems since you only pay for the amount of services or storage you use. Cloud can also store this large amount of data. It allows doctors quicker access of the patient s previous medical records. Doctors can also collaborate and share data easily. Another benefit of using cloud computing for healthcare applications is the mobility it provides. On site paramedics can also use the data on cloud to triage patients and update their status, therefore making emergency services much quicker. Scheduling of appointments is also automated using cloud computing. In this paper we have analyzed cloud computing models which are applied in healthcare. The paper is organized as follows, section 2 discusses the literature review. Section 3 tabulates the results of the comparative study. Section 4 summarizes the conclusions of the study. 2. LITERATURE REVIEW 2.1. Basic Cloud Implementation Anusha Bamini et al [1] in her paper explains how the manual method of creating and maintaining a patient s data repository resulted in frequent errors and did not support portability or ease of access and therefore cloud computing technologies were adopted for In this system the collection of patient data is automated using sensors attached to existing medical equipment which is directly uploaded to the cloud. This data can then be analyzed and intelligent processing of activity information[1] is tested. A user interface which is compatible with existing web applications and supports ubiquitous health monitoring is a necessary part of the system. Certain security measure for protection of data are implemented but they are minimal and not effective enough. The advantage of this method is that it automated collection of patient data and minimizes data retrieval time. The disadvantage is that it is not very secure and each component is a point at which security can be breached. 11

2 maintaining the healthcare system. The basic cloud system architecture is described in Figure 1. Fig 1: Illustration of cloud computing technology in healthcare [1] 2.2. Hybrid Open Cloud Model Sherif E. Hussein[2] proposed a hybrid model in which private cloud is implemented using OpenStack software(see Figure 2) which implements the cloud management software, the hypervisor, legacy migration and private cloud security measures. The low cost and high scalability of public cloud makes it the ideal backup for the private cloud. Fig 2 : OpenStack Architecture. [2] 12

3 It provides expandability of the system and provides auxiliary services. Two scenarios are implemented. In the first scenario all the workload is handled by the private cloud. In the second scenario the workload is transferred to the public cloud when the load is too much for the private cloud. This model results in a better performance at a lower cost due to its versatility in selecting resources at different times from different types of clouds Hybrid solution for privacy preserving medical data sharing Ji-Jiang Yang et al.[3] proposed a hybrid solution which vertically partitions the medical dataset with different privacy protection levels. Thus private patient data like their name, date of birth, age etc. is provided highest level of protection and is stored as cipher text. The partition that can be used for analysis is stored as plaintext. The third partition stored quasi identifier which are used to relate the two tables. Provisions are also made for data merging such that if a user has appropriate authentication then he may access the partition required for analysis and then using the quasi identifiers he can match patient data. Integrity checking is done both locally by the data recipient and remotely by the data owner[10]. The framework is described in Figure 3. The advantages of this model are that it provides varying levels of security to the data thereby protecting the sensitive data without hindering performance of the system. It borrows from all the best aspects of previously established privacy policies. The disadvantage however is that the performance of the system under heavy load is unknown. Thus each of these models have their merits and demerits. The first model is already in use and therefore its shortcomings have been encountered. The second method aims to overcome one of the shortcoming by providing a balance between security and resource allocation. The third method overcomes another challenge by providing high levels of security while minimizing querying time. 3. COMPARATIVE STUDY In this section we list down the results of our comparative study. The parameters under consideration range from objectives and results to security and performance. Comparative study Objective Principle idea Components Types of cloud used Tools used Architectures Traditional Cloud Model.[1] Hybrid open cloud model.[2] Hybrid solution for privacy preserving in cloud.[3] Patient s symptoms, lab reports, diagnosis and other medical details are uploaded to a cloud based information repository. This information can then be retrieved and analyzed. To develop a system which minimizes human error and provides correct and complete information necessary at the location at a particular time.[1] 1.)Patient s Data Collection 2.)Cloud Computing Security 3.)Hosting Data Analysis Service 4.)Intelligent Manipulation of Human Activities 5.)Cloud Computing Framework This paper introduces a hybrid cloud architecture which uses a private cloud to store medical data since it is secure and uses a public cloud for scaling, non-core applications and as a backup data repository. The private cloud is utilized to store patient data while computations and collaboration related components can be kept on the public cloud.[2] 1.)The cloud management software 2.)The hypervisor 3.)Legacy migration 4.)Private cloud security measures This paper proposes a hybrid privacy policy which provides multiple security measures to protect data while it is stored and while it is being shared. The proposed system also implements multiple levels and types of security. Combining benefits of privacy by policy, privacy by statistics, and privacy by cryptography while overcoming their deficiencies.[3] 1.)Vertical data partition 2.)Data merging 3.)Integrity checking 4.)Hybrid search Public/Private. Public+(OpenStack)Private. Third party cloud provider. OpenStack[9], OpenShift, HDFS and HBase in Deltacloud, CloudSim and Mongodb. Hadoop platform, OpenCloudCare, Context aware Activity Manipulation Engine 13

4 Security provider (CAME) and the Human Activity Recognition Engine (HARE)[5], User Interface as a Service (UIaaS)[6] OpenCloudCare for remote patient health monitoring.[4] HP Enterprise Secure Key Manager (ESKM) and Atalla Network Security Processors (NSP), Dome9 for OpenStack(private cloud). Microsoft Windows 7and Visual C++ version 6.0, The methodology proposed in this paper which is a combination of the existing approaches of privacy by policy, privacy by statistics, and privacy by cryptography. Encryption - HP Atalla Encryption method AES, OFB 128 bit Hash method SHA-1 Test scenarios for performance evaluation Clients involved Types of tasks simulated - In order to evaluate efficiency of hybrid cloud architecture with respect to increasing execution speed two test scenarios were simulated: 1.) All workload was processed locally within the private cloud. 2.) The workload (tasks) could be migrated to public clouds in case private cloud resources (hosts, VMs) were busy or unavailable.[2] In currently established systems several clients are using the system simultaneously. Upto 20 clients are simulated. - Different tasks were executed and evaluated. In order to comparatively evaluate the proposed hybrid search methodology two approaches are used: 1.) TFIDF: The similarities between queries and EMRs are computed by summing the product of the term frequency and inverse document frequency for each query term [7]. 2.) BM25: The relevances between queries and EMRs are calculated based on the BM25 formula [8] Only two clients are simulated: 1)Hospital A 2)Doctor B Only search queries were simulated and evaluated. Final Result Advantage over previous method Disadvantage 14 A basic architecture of cloud computing technology in healthcare. Automation of patient data collection and maximization of data access. Slow data transmission, security risk since discrete security components are not taken into consideration. A hybrid infrastructure implementation using cloud which provides optimal resource allocation at a low cost and allows the system to dynamically expand its capacity. The hybrid architecture provides maximum resources, optimal resource allocation, a balance between security and advantages of cloud technologies all at a low cost. The security concerns are outsourced to other systems, but how these protection mechanisms will function in the proposed architecture is not addressed is greater detail. A hybrid approach to improve security of individual privacy in the cloud. This system combines the best aspects of previously adopted privacy implementation strategies and overcomes their shortcomings to generate a hybrid privacy protection mechanism for healthcare data in the cloud. The simulation does not address the reaction of the system with multiple clients and large loads. The Methodology also does not address the issue of legacy migration.

5 The analysis of these three architectures reveals that since the first method is already established, the load that it can handle is known. Whereas in the second and third papers only simulations have been performed hence the maximum traffic they can withstand is unknown. In the traditional cloud model there are more number of components and security breaches occur at each of those components. In the hybrid architecture the security provisions are outsourced and therefore not tailored to the architecture. Whereas in the third method high levels of security are provided by encrypting and partitioning data. 4. CONCLUSION Hospitals need to have an appropriate flow of information in order to function successfully. A cloud system can provide such a flow of information and can also be used to analysis of the information. There are several challenges in applying cloud computing to the healthcare infrastructure. However there are several models which overcome these challenges of which we have reviewed three in this paper. We have reached the conclusion that of all the three models the third model, hybrid model to implement privacy protection[3] overcomes maximum of the shortcoming and provides maximum services as well. REFERENCES [1] Bamini, A. and Enoch, S Role of Cloud Computing in the Provision of Healthcare. Doctors Academy Publications, July 2011, Volume 1,Issue 1. [2] Huessein, S.E. and Arafat, H An Open Cloud Model for Expanding Healthcare Infrastructure. (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 4, No. 9, [3] Yang, J.J., Li, J.Q. and Niu, Y A hybrid solution for privacy preserving medical data sharing in the cloud environment. Future Generation Computer Systems. [4] Mouleeswaran, S.K., Rangaswamy, A. and Rauf, H.A Harnessing and Securing Cloud in Patient Health Monitoring. International Conference on Computer Communication and Informatics, Jan 2012, p [5] Khattak, A.M. and Pervez, Z Intelligent Manipulation of Human Activities using Cloud computing for u Life Care. 10th Annual International Symposium on Applications and the Internet, [6] Mayadimitrova and SiyaLozanova, 2011 Cloud Computing Framework for New Medical Interface Technologies. spreer.eu. [7] Robertson, S.E., Walker, S., Hancock-Beaulieu, M., Gull, A. and Lau, M Okapi at TREC. Text Retrieval Conference. [8] Golle, P., Staddon, J. and Waters, B Secure conjunctive keyword search over encrypted data. Proc. of ACNS, 2004, pp [9] Girish, L.S. and Guruprasad, H.S Building Private Cloud using OpenStack. International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), Volume 3, Issue 3, May June [10] Ateniese, G., Burns, R., Curtmola, R., Herring, J., Kissner, L., Peterson, Z. and Song, D Provable data possession at untrusted stores. CCS07, ACM, Alexandria, Virginia, USA, 2007, pp Oct, Nov. 15