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A Cloud Computing Solution for Securely Storing and Accessing Patients Medical Data N. Pradheep, Research Scholar, Department of Electronics, Erode Arts & Science College, Tamilnadu, India. E-mail:
[email protected] M. Venkatachalam, Head & Associate Professor, Department of Electronics, Erode Arts & Science College, Tamilnadu, India. M. Saroja, Associate Professor, Department of Electronics, Erode Arts & Science College, Tamilnadu, India. S. Prakasam, Research Scholar, Department of Electronics, Erode Arts & Science College, Tamilnadu, India.
Abstract--- Nowadays digital form of storing is adopted and replaced the traditional storage. Storing them on-site is not an efficient solution due to issues like scalability and interoperability. Cloud technology provides an attractive solution by offering lower cost, excellent scalability and moreover fault tolerant method. Even though cloud technology promises many such provisions, there are many critical issues to be considered like security, privacy, authentication, authorization which must be overcome to enjoy the full benefits of the cloud technology. Authorised persons, those who are certified by the agencies should have access for viewing and processing it. An innovative technology/system is proposed for storing the images securely, by providing security and authentication. Proposed technology supports three security aspects, i) authentication, ii) integrity and iii) confidentiality. Authentication is done by providing certificates to users i.e. to agencies and persons authorised by the hospital. Confidentiality is provided using encryption done using both Arnold cat map and the diffusion process. Integrity is maintained by using hash value. It computed by logistic map for encrypted image data which adds additional diffusion. Keywords--- Cloud Computing, Medical Images, DICOM, Encryption, Confusion, Diffusion, Integrity and Authentication.
I.
Introduction
Traditionally standalone applications ran on a single machine, and all the users’ data were stored locally. Nowadays the volume of the data is increasing to an enormous amount, therefore storing, accessing and computing them locally is not an efficient solution and probably an impossible one [4]. This led to the need for cloud technology derived from grid, pervasive, utility computing techniques. The cloud computing technology is a model for easy, ubiquitous access to a pool of resources like network, storage, computing power, etc., which can be obtained on demand. Cloud computing is delivering computing and storage capacity to a heterogeneous community of end-recipients as a service. Three characteristics differentiate the cloud from traditional hosting are, i) on-demand usage and charge per usage, ii) type of service option and iii) fully managed by the provider. Moreover, the resources can be acquired and released with minimal interaction and management. Virtualization is widely used in the cloud technology to provide a uniform interface to resources that are dynamically scalable, which abstracts the physical heterogeneity and geographical distribution. Today’s Pricing models are based on normalised CPU-hours, MB network I/O, and GB/day storage or based on a licensed cloud product and can be used along with local physical resources. Medical Data In healthcare industry [1], the patient’s medical data plays a vital role because diagnoses are made only by those images. Due to the recent advancements in the medical field, the volume of data and the computing power for processing them are growing continuously. The accumulated data in the healthcare industry sees a sudden increase, due to the seasonal changes lead to diseases like influenza, viral, and moreover, the situation could go further unpredictable due to natural disasters. More diagnostic imaging procedures will be performed in U.S during the year 2014 which will lead to 100 Petabytes of data being generated [2]. This massive amount of data and needs for storing, archiving and processing need some service. To cope up with all these demands, cloud technology can offer a better solution. By using cloud projects, patients’ medical data are collected and stored in the cloud [3], and access is provided using web portal which acts as a user interface for the users to interact with the system. Since the medical data need to be communicated among the doctors for taking efficient and faster decision for the treatment of patients, a standard format must be followed across the hospitals [4]. This has led to the development of DICOM (Digital Imaging and Communications in Medicine) standard. This standard includes the format for maintaining the
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medical data in the *.dcm format. Outsourcing development activities or using cloud resources introduce many risk factors, which prevent healthcare organisations from using these potentially advantageous resources. Currently, there are many cloud projects going on in the healthcare industry. Examples include i) Cisco healthcare services uses cloud services, ii) Visiting Nurse Service system powered by Salesforce.com [5]. There is a need to develop a healthcare system for handling patient’s medical data using cloud technology and to provide security to the patient’s medical data that reside in the cloud. In this paper, we have proposed a framework for securely storing and retrieving the patient’s medical records that help the hospitals and scan centres to maintain their medical images occupying large storage space, using cloud computing. Since the patient’s data is outsourced to a cloud, there is a fear of security breaches. The main issues that are to be considered in cloud security [6] are security (how secure the users data are), privacy (is the data being kept confidential), reliability (even if the systems are down the cloud must be able to serve), compliance to standards, long-term viability (persistent storage), freedom (users can’t control the location and the computation in the cloud), solutions (encrypt before storing). There are some aspects of security that can be provided such as authentication, confidentiality and integrity. Authentication is done using building a separate third-party certification authority. Confidentiality can be ensured by using light weight image based encryption algorithms adopting chaotic maps. Chaotic map encryption[7, 8, 9] is well suited for image encryption because of its high correlation property and high redundancy. The last aspect of security that we consider is the integrity which can be provided by the logistic map which is a kind of one-way function so that it is suitable for hash computation. The paper is organized as, Section 2, details the related works done in the cloud healthcare field, Section 3 describes the system methodology involved in it, Section 4 presents the result analysis and Section 5 concludes the paper.
II.
Related Works
In olden days, the information about the patients is collected manually and then they are entered through the data entering the terminal and then they are sent to a database from where the clinicians, if needed, can access them. However, this became a very inefficient solution because it took more time for examining the data once it is collected. It is also error prone because a human makes mistakes while entering the data or while collecting. Therefore a method is proposed which combines the cloud technology along with wireless sensor network technology to provide a very efficient way of collecting the data and then accessing them. In this model, there are wireless sensors that are being attached to the medical devices, so that they get the accurate data about the patients’ ailments. The obtained data is then transmitted thru a wireless network to the exchange services that are available which look which appropriate storage service in the cloud is to be used and store the data there. And if any staff or clinicians need the data, they request the exchange services and access the data. The merits of this kind are, i) realtime data collecting, ii) available all the time and iii) since wireless networks are used, no cabling is necessary. EMR (Electronic Medical Record) is a digitalized way of maintaining the patients’ data in the hospitals. The main advantages are, it provides an efficient way of collecting the data and also reduces the errors which are caused by manual entry. MIFAS (Medical Image File Accessing System) [4] is built on the top of Hadoop distributed file system and co-allocation mechanism of cloud so that the system runs on a cloud environment. Hadoop is used for storing files across multiple nodes, and it helps in replicated file storage for efficient accessing. Name node helps in name resolution which suffers from a single point of failure. The co-allocation mechanism was mainly introduced for parallel and faster downloading of files from multiple sites at the same time for faster execution. The system is built on a cloud system with the HDFS as a PaaS (Platform as a service). Upon this layer is the MIFAS middleware which has three components 1) Information service, 2) Co-allocation and 3) Replication location service. Initially, medical images were stored onsite (in their storage) which was enough when there are fewer amounts of data. However, as days go on, there are an enormous amount of data to be stored, retrieved and processed. For that purpose, traditional onsite mode of processing needs very high maintenance costs. Hence the healthcare industry needs a cost effective solution which is offered by the cloud. Brigham Young University Information Technology (IT) students have implemented a medical archive solution [2] using DICOM. They have implemented this on the platform offered by Microsoft Windows Azure platform. This project has three major components: DICOM server, DICOM image indexer and Web-based UI. When a request is received for storing an image, the request is got by the running instances of web UI and the DICOM server. The request is then passed on to the image indexer which extracts the tag information from the images and stores them separately in the SQL Azure database. The instances will have only the current request
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details. Suppose if the instance fails before storing the image, the details of metadata can be given to other instances running so that it achieves recoverability. The rich set of features makes Azure the best platform for medical imaging archival solutions. However, it lacks in security measures and they must be implemented in Azure to make to be a very appropriate one for medical imagery.
III.
Proposed Framework
Figure 1: Architecture Diagram for the Proposed Work Figure 1 shows three different layers i) user interface layer (web portal), ii) proposed work layer and iii) the cloud layer. These layers are provided with different functionalities like the OSI architecture in networking model. Moreover, all the layers are interconnected such that the inputs and the outputs are exchanged among them. The users interact with the proposed framework i.e. i) hospitals, ii) scan centres and iii) doctors. The scan centres technicians may be within a hospital or in an individual scan centre. The doctors are from the hospital; the hospital should certify them that they are a part of their organisation. The users interact with the system through the user interface layer which is connected to the internet. There is a web console layer, which acts as the user interface to the end users. The end user, via the internet interact with the web console layer in a sense the user provides the details in the web console and those details are forwarded to the CA (Certification Authority) for authentication. Confidentiality is the security aspect where the data is maintained in a secured way. Only the authorised persons must be able to view the original data, and for the rest, even if the data is visible it must be meaningless so that any meaningful information cannot be acquired. Proposed word deals with the DICOM files where the confidentiality is maintained with the help of chaotic methods. Integrity is the security aspect where the data is maintained without any alteration. The data must be as such it was created because it loses its original meaning even if a small part of it changes. Integrity is used for maintaining the originality of the data. In this paper, we maintain the integrity by using logistic hash maps. Authentication is another important security aspect which decides the users who are allowed to view the data. Authentication is provided by username and password. In this paper, we provide additional authentication by providing certificates to the users who are authenticated by the external third-party Certification Authority (CA). In the proposed framework, layer 2 shows the security methods provided, i.e., confidentiality, integrity and authentication. Forthcoming sections elaborate each security types, their importance and how they are used. Certificate Authority (CA)- is an external party used for providing certificates so that the authenticity is maintained. CA guarantees the individual who claims his/her correct identity is granted the unique certificate. The certificate issued by the CA is X.509 digital certificate. It has many of the fields like version, serial number, algorithm ID, issuer, validity, public key algorithm, subject public key. The proposed method uses PEM (Privacy Enhanced Mail) format certificate extension, which is in the format like Base64 encoded certificate. Authentication- Once the user details are received by the CA, they get stored in the database which CA maintains. If any of the user requests for accessing the system, then they must request a certificate to certify them that they are authenticated users. For that, the users are provided with the certificate when they are requested based on the confidential details that are already present in the CA’s database. Once the users get the certificate, they can access the system. Thus authentication is established.
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Confidentiality- Since the cloud service provider is a third party, the data that are going to be getting stored needs to be protected. So the data must be encrypted in some means to provide confidentiality. In our proposed framework, improved chaotic cat maps are used to encrypt the medical images with some key like parameters. The reason for choosing chaotic based image encryption technique [10, 11] is because images are to be encrypted. Image encryption cannot be done efficiently with traditional text encryption based algorithms. Reasons, why traditional encryption algorithms are not good for images, are, i) high redundancy, ii) bulk capacity make encrypted image data vulnerable to attacks via cryptanalysis, iii) image data have strong correlations among adjacent pixels, and iv) compression problems [12]. Integrity- Hash function is used for providing the integrity, which takes a variable length data as input and outputs a fixed length digest. Traditional hash algorithms do not fit for images; a new direction has emerged in calculating the hash for images [13]. It led to calculating hash based chaotic theory which deals with dynamic systems. Some chaotic maps like logistic maps [7] are a kind of irreversible map which produces a confused image while reversing. Use-case Diagram Figure 2 depicts the proposed framework use case diagram. It contains 3 actors i.e. i) hospitals, ii) scan centres and iii) doctors and Use-Cases such as i) CA, ii) Login, iii) Authentication, iv) Cloud Controller, v) uploader and vi) downloader. The end users are doctors in the hospitals and the technicians both in hospitals and the scan centres. They use the framework and use the certificate which is provided by a third party certification authority to access the cloud storage. The end user can login using the web console that is provided as a user interface for accessing the cloud storage. As a first step, hospitals provide the list of doctors, and the scan centres provide the technicians who are authenticated to use the storage for download and upload patients’ medical data. Those details are stored in the certification authority’s database. After this, the users (doctor or technician) can use the system by requesting the certificate from CA. The CA will create the certificate based on the details provided by the users and issue it to the user. The user can now use the system with the certificate that is obtained. The two types of users are offered with different options for using the system. The technician can upload the DICOM medical data into the cloud storage.
Figure 2: Use Case Diagram for the Proposed Work The doctors can download the data for diagnosis purpose. When the technician uploads the data in the cloud storage, many security measures are to be undertaken for providing secure cloud storage. Whenever the uploading takes place, the data is being encrypted and stored for ensuring confidentiality. Authentication is provided using CA. Before the data is stored, the hash value is being computed for ensuring integrity. Mathematical Analysis Cryptographic ciphers adopt two principles for the betterment of cipher; they are confusion and diffusion [9]. Confusion obscures the relationship between the plaintext and ciphertext, while Diffusion means spreading out the influence of a single plaintext digit over many cipher text. Confusion, on the other hand, transforms and complicates the dependence of cipher text on plaintext. These two principles are closely related to the mixing and ergodicity properties of chaotic maps [7].
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Confusion- In this process, Baker map, Cat map and the Standard map [14, 15], is used to realise the confusion of all pixels. Chaotic maps have a close relation with cryptosystems security. First, its parameter, sensitivity of parameter should be high, which in turns makes the cryptosystem stronger. Secondly, the initial-value sensitivity and state ergodicity of the chaotic map determine the confusion strength [11]. Thus, higher state ergodicity means more random confusion process and more difficult for statistic attack. Therefore, the chaotic map with high initialvalue sensitivity and state ergodicity is preferred. Diffusion- The encryption process includes applying the cat map and then applying the diffusion process [16]. Since image data is highly co-related, there is a possibility for known image attack. This can be avoided by the diffusion process [8] which makes the statistical relation between plain text and the cipher text very harder to predict. Before applying this diffusion process, the hash is computed to provide integrity for the medical images. After performing these security activities, the data is stored in the Cloud. Arnold Cat MAP Let X is a metric space. Then a map 𝑓𝑓: 𝑋𝑋 → 𝑋𝑋 is said to be (Devaney) chaotic on X if it satisfies the following conditions [15]: “f” under initial conditions exhibits the sensitivity dependence and is topologically transitive.
The Arnold Cat Map is a discrete system that stretches and folds its trajectories in phase space as shown in Eq. 1 𝑥𝑥 𝑥𝑥𝑖𝑖+1 1 𝑝𝑝 � � 𝑖𝑖 � 𝑚𝑚𝑚𝑚𝑚𝑚 𝑁𝑁 �𝑦𝑦 � = � (1) 𝑞𝑞 𝑝𝑝𝑝𝑝 + 1 𝑦𝑦𝑖𝑖 𝑖𝑖+1 where 𝑥𝑥𝑖𝑖+1 and 𝑦𝑦𝑖𝑖+1 are the pixel positions of the cipher image, 𝑥𝑥𝑖𝑖 and 𝑦𝑦𝑖𝑖 are the pixel positions of original image, N is the number of columns considered while applying the cat map [17]. The values ‘p’ and ‘q’ indicate the parameters which must be kept secret. They are like the key values. One more important condition of cat map is that it must be area preserving. To achieve this, the determinant value should be 1. Hash Algorithm The hash algorithm is applied after the confusion process. Calculating hash after diffusion will lead to collision due to the property of diffusion function. For each image of the DICOM files do: Step 1: Step 2: Step 3: Step 4: Step 5: Step 6:
IV.
Calculate SHA-512 for the image which produces a 128-byte message digest Divide 128 bytes into eight 16 bytes Calculate XNOR for the pairs, producing four outputs Apply logistic map, with key obtained in step 3. Concatenate the results. Apply MD5 for the value obtained in step 5, producing the final message digest for the given data.
Results and Analysis
Figure 3: Working Model of the Proposed Framework
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Figure 3 shows the configuration of systems involved in the model adopting our proposed framework. Totally five systems are included in the model. First one acts as an end user machine which runs in a normal Windows. The second one is the Certification Authority which provides authentication running in Windows. The third one is the Server that provides confidentiality (both confusion and diffusion) running in OpenSUSE Linux flavour OS. The other two systems implement a private Eucalyptus Cloud running in CentOS. CT, MRI and PET are the medical scan files that are taken into consideration. The Table 1 shows the type of DICOM files taken, their corresponding size and number of frames. Table 1: Input DICOM Medical File Features S No 1 2 3
Image Type CT MRI PET
Size (MB) 39 9 11
Number of Frames 52 19 227
DICOM files (CT-FEROVIX, MRI-WRIX and PET-THORAX) are used in this paper for evaluating the proposed algorithm. The size of the DICOM file (CT scan of lungs) is 39MB, and after converting it using Rubo DICOM viewer, it is divided into 52 frames. When the images are encrypted using Arnold Chaotic Cat Map, the pixels are rearranged and a shuffled image is obtained. Proposed method applies both Chaotic Cat Map and with diffusion and confusion process, which improves more security. Table 2 shows the PSNR values obtained, which shows the quality of images after applying the i) proposed a method, i.e. diffusion and confusion, ii) only with confusion and iii) only with diffusion. Table 2: PSNR Values Bit rate [kb]
Confusion
Diffusion
350 360 380 390 400
33.83 33.26 33.35 33.14 32.94
42.99 42.6 42.6 42.35 39.06
Confusion and Diffusion [Proposed] PSNR[dB] 43.41 45.93 41.97 41.74 40
Figure 4: Bit Rate[kb] Vs PSNR[dB] Table 3 shows the message digests generated by the DICOM images taken under study and after applying confusion. A random bit in an image is changed and the message digest is generated and compared with the original image message digest shown in Table 4. Table 3: Message Digests for Various Images
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Hash Value DICOM images
Hash Value After confusion
dbfba83ff1532c94d90d85dfe2de828
91bb4fefe246308a17d16e36efd9e5
e665305217c723a3cf3802310eacf2e
0b81f3066191ead6b18337baec6f97
3ad46f25268952bdfa17e6a1d1786df
5802f25d5bece1e741cd12c363cecc
1697cb8ba4c40858fd152414fc3daeb
7d280caca60b370f0b182b268e62a3
e2a64ec5b8a1db0401c026ef938e5e6
5b25d654e9beb5589a09f503b770c
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Table 4: Message Digests After Bit Change Hash value for DICOM images dbfba83ff1532c94d90d85dfe2de828 e665305217c723a3cf3802310eacf2 3ad46f25268952bdfa17e6a1d1786d 1697cb8ba4c40858fd152414fc3dae e2a64ec5b8a1db0401c026ef938e5e
Hash value After one-bit change 2781b4fb71b0ebb7bcea1ef776c425c7 b37e49729158afdd8d0e1539adc387a1 da52e005b51377ca7f3fae35f7dad744 c20931bf34df388fa71211b4149faa47 159513702c1b5452ddcdc98fb8bd777b
The equation, 𝑃𝑃 = (𝐵𝐵/128) ∗ 100% where B is the average number of bits changed while a random bit gets changed due to some attack or modification by unauthorized person. Table 5 shows the individual values of P for the images that are taken in study. The average rate of bits changed (P) is 52.80 for all those images. Since the hash algorithm relies on each and every bit of the image, the resulting message digest is very hard to find and also to infer the plain text from the message digest. Table 5: Average Rate of Changed Bits Input Image Image 1 Image 2 Image 3 Image 4 Image 5
Number of bits changed in resultant image out of 128 when random bits are changed in input image 68 69 65 70 66
Rate of change of bits 53.12 53.9 50.78 54.68 51.56
A typical DAS (Directly Attached Storage) system is made of a data storage device connected directly to a computer through a host bus adapter. Since the storage is attached locally, it is felt that it is the highest security for any confidential data. SAN (Storage Area Network) can offer fault-tolerant design in many areas: controller redundancy, cooling redundancy, and storage fault tolerance patterns. However, the SAN controller must take care of the devices like an administrator. Table 6 shows the benchmark results that were obtained when the cloud storage is compared with ordinary storage and the SAN for storage of DICOM medical files that are taken into consideration. The proposed framework was tested by using these benchmarks, i) size of the data, ii) block size vs. download time, iii) block size vs. upload time, iv) maintenance, v) security problems, vi) concurrent access, vii) scalability, viii) reliability, ix) availability, x) IOPs, xi) Mbps and xii) IO latency. Table 6 shows the benchmark results. The size of the data matters a lot while considering all the three kinds of storage. Since cloud offers unlimited storage, it is better to go for cloud for large sized DICOM files [20,21]. Downloading or uploading large sized file's matters in the case of cloud storage whereas, in the other two, it is faster because the data is present locally. The major drawback of using cloud storage is that security is not provided to the level as in the cases of the other two. Because it is the storage offered by a third party provider. Other security issues like reliability, maintenance, availability and scalability are addressed by the Cloud Storage Provider (CSP), but they must be done manually in the other two. Since the ordinary and SAN storage offers local-like storage, their IOPs and MBps are higher. However, in cloud storage, it all depends on the internet speed. In the case of IO latency too, cloud’s performance is based on the internet speed. The major advantage of using the cloud storage is for larger space, and some security problems as mentioned above are addressed. Table 6: Benchmarks Obtained Amount of data Block size Vs uploading Block size Vs downloading Maintenance Security problems Concurrent access Availability Scalability Reliability I/Os per second (IOps) Megabytes per second (MBps) I/O Latency
V.
Ordinary storage Limited Faster Faster Manual Very secure Data inconsistency Until any problem occur Not scalable Yes Higher Higher Very less
SAN Unlimited to an extent Fast Fast Administrator Secure Allowed Until any problem occur Scalable to a limit Yes Higher Higher Lesser
Cloud storage Unlimited Depends on block size Depends on block size CSP takes care Not secure CSP takes care 24 X 7 Fully scalable Limited Based on internet speed Based on internet speed Based on internet speed
Conclusion
In this paper, we have proposed a framework for storing and accessing the patient’s medical data securely using the cloud. The design of web console, certification authority and the encryption/decryption using improved Arnold
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cat map and diffusion function is done and results are verified with the help of PSNR values. Integrity for the encrypted medical data is calculated and proved to be more efficient than traditional hash methods that apply only to text data. If an authenticated user needs to retrieve it, the integrity is checked, decrypted and presented to the user. The proposed framework can be further extended by providing authorization to the DICOM files (i.e.) having an access control list depicting the kind of operations the authenticated users can perform on the DICOM files, and it also can be extended such that the application can be accessed using mobile phones which provide pervasive access to the files in cloud.
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