Topic: BusinessIndustry

Last updated: October 29, 2019

1 1. INTRODUCTION 1.1 CLOUD COMPUTING The cloud computing is a model for enabling convenient on demand, network access to shared pool of resources (network, server, storage, application).

The technology of distributed data processing in which some scalable information resources and capacities are provided as a service to multiple external customer through internet. The main aim of cloud computing is move to desktop computer to service oriented platform. The cloud computing application, data and resources are provide to users as a services over the web.

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The service provided may be based on low in cost, massively scalable, on demand based. Figure1: Basic Cloud Storage Model2 1.2 REASON FOR ADAPTING CLOUD COMPUTING On Demand Service A consumer can have a provision of computer capabilities, such as a server time and network storage, as needed automatically without requiring human interaction with each service provider. Working from Anywhere The cloud computing is a model driven methodology that provides configurable computing resources over the internet service. The cloud resources can be accessed from anywhere in the world. Rapid Elasticity and Cost Saving The cloud capabilities can rapidly scale in and out the resource quickly. The main goal of cloud can eliminate the capital and operational cost. Because it provides various pooling of resource.

1.3 USAGE OF CLOUD IN THE HOSPITAL The hospital environment, have multiple computers that can be used in medical rooms. Each room needs proper network accessibility, hardware and software which is used to upload, store and retrieve the patient information or other medical data. The electronic patient health record contains the overall history of a patient. Scalability Real time health records are generated. Each hospital must keep the medical records for at least 15 years. Mobility Cloud can increasing the demand of physicians time. The physicians are needed to access the patient record in a remote way.

So that doctors can access the patient record easily and verify the patient situation.3 Sharing Cloud computing can provide the better relationship between the patient and doctor. The healthcare service provider can access the complete patient information easily through online. The electronic medical record in used to reduce the repeat diagnostic tests, saving time, memory, and patient stress. Many organizations now using daily updatable or changeable data. For keeping data both security and usability cloud computing provide the environment to store data on different cluster. Various organizations (e.g.

, Hospital authorities, industries and government organizations etc) freeing person specific data, which called as private sensitive information. They provide information of privacy of persons. The preserving privacy is protecting for individual’s sensitive information on a public platform. Unluckily de-identification of persons even by neglecting denotative identity like name, SSN, Voter Id number and license number.

Data anonymization is the best way to preserve privacy over the personal privacy sensitive information. The data anonymization approach is very efficient technique but if the scalability of the data set like private sensitive information is increased the anonymization technique fails to preserve privacy. So scalable big data privacy preservation in cloud can be provided. The purpose of this project is to develop an environment to provide privacy over the personable sensitive data. The Major aim of the work is to develop a tool for patients to give medical care providers more insight into your personal health information. Main aim of privacy is provide secure data and for external knowledge. This application can helps to view the patient’s health records only for authorized persons.

4 2. LITERATURE REVIEW 2.1 INTRODUCTION In this literature review journals related to Privacy preservation and file encryption techniques are revised to get an idea to carry out the process of this project. The revised survey papers are listed.

2.2 LITERATURE REVIEW J. Xu, W. Wang, J. Pei, X.

Wang, B. Shi, and A.W.C. Fu, “Utilitybased Anonymization using Local Recording”. This paper explained the clustering techniques has been improved or enhanced to achieve a privacy preservation in localrecoding anonymization. From the utility privacy preservation perspective the local-recoding anonymization has been studied.

It also uses the top-down counterpart and a bottom-up greedy approach are together pit-forth based on the cluster size, the agglomerative clustering technique and divisive clustering techniques get enhanced. B. C. M. Fung, K. Wang, R.

Chen, and P. S. Yu, “Privacy-Preserving Data Publishing: A Survey of Recent Developments”. In this paper Data privacy preservation has been investigated extensively, existing approaches for local-recoding anonymization and models for privacy are reviewed briefly. Also, the research for scalability issues in existing anonymization approaches are surveyed shortly. To address the local-recoding anonymization as the k member clustering problem where the cluster size should not be less than k in order to achieve k-anonymity, For that the simple greedy algorithm are used. Xu.

Zha , D. Won, Chi .Yang, Jini .Chan, “Proximity Aware Local Recording Anonymization with Map Reduce for Scalable Bigdata Preservation in Cloud”. In this paper local recording for big data anonymization against proximity privacy sensitive information is discussed.

In proximity preservation used the two pharse clustering approach of t-ancestor algorithm. This method used to improve the scalability and time efficiency. Xuyun Zhang, Chi Yang, Surya Nepal, Chang Liu, Wanchun Dou, Jinjun Chen, “A MapReduce Based Approach of Scalable Multidimensional Anonymization for Big Data Privacy Preservation on Cloud”, Xuyun Zhang investigated the scalability issue of5 multidimensional anonymization over big data on cloud. The main issues of bigdata is scalability to finding the median of multidimensional partitioning, But ensuring privacy preservation of large scale data sets still needs extensive investigation, it is integrated into scalable and cost effective privacy preserving framework based on mapreduce method. R. Sreedhar, Dm. Uma, “Bigdata Processing with Privacy Map Reduce Cloud”, According to the author the privacy preservation techniques use k- anonmity approach.

This paper introduces the map reduce framework to anonymize large scale of dataset using two pharse top down algorithm. In map reduce framework of optimum balanced scheduling method is used to improve the privacy of sensitive dataset. In privacy preservation map reduce method it use the TP-TDS approach to improve the scalability of individual dataset in sensitive field. D.Chha, H.A.

Girija, K. K. Raja, “Data Anonymization Technique for Privacy Preservation on Map Reduce Framework”. Describe the author of data anonymization technique to hide sensitive data in the cloud to avoid risk. The existing review paper of privacy preservation used the k-anonymity approach with two pharse top down algorithm. In additional use the method of I diversity is used to access the data conveniently in the cloud. N.

Ken, Y. Masa , S. Hisa , S.

Yoshi , “Privacy Preservation Analysis Technique for Secure, Cloud Based Bigdata Analysis”. In this paper describe the privacy preservation in the cloud based on statistical analysis and some secure mechanism. Hitachi has describe a privacy preserving analysis technique .

In this technique is used to analyze data based on sequence steps for privacy preserving analysis. Encryption technique is used in the common key searchable method. It provide the efficient access between the user and cloud provider. S. S. Bhanu, D. Sakthi , “A Review of Privacy Preservation Data Publishing the Health Care”.

In this paper describe the privacy preservation data publishing of electronic medical record system are used. They are used two different techniques are anonymization and encryption approach. The healthcare data uses some anonymization approach namely single anonymization and multiple anonymization technique. P.Ashi, S.

Tejas, J.Srini, D.Sun, “Medical Application of Privacy Preservation by Bigdata Analysis using Hadoop Map Reduce Framework”. In this paper describe the large scale of data analysis at optimum response time. The author implement the privacy terms at medical application by using hadoop frame work.

The proposed system is divided into two major6 components sensitive disclosure flag and sensitive weight. Classification algorithm is used to indicate the efficiency of work. K. Priyanka, P.

Sneha, “Securing Personal Health Records in Cloud Using Attribute Based Encryption”. According to the author his aim is secure access of personal health records based on attribute based encryption. In PHR scenario there are multiple security mechanism, particularly CD-ABE and MA-ABE approach are used. The security mechanism is used to transmit the personal health records securely. Zhang X, Liu C, Nepal S, Pandey S, Chen J.

” A Privacy Leakage Upper Bound Constraint-Based Approach for Cost-Effective Privacy Preserving of Intermediate Data sets in Cloud”. In this paper, proposed an approach that identifies which part is intermediate of datasets. And its needs to be encrypted.

Generate a tree structure based on relationship between the intermediate datasets to analyze privacy propagation among datasets. Main problem of existing system is analyze the intermediate dataset. Because it is need the intensive investigation.

Contributions of this paper, planning to investigate privacy. Efficient scheduling of intermediate datasets in cloud take privacy preserving. Optimized balanced scheduling strategies are expected to developed highly efficient privacy aware dataset scheduling. G. Aggarwal, R. Panigrahy, T. Feder, D. Thomas, K.

Kenthapadi, S. Khuller, and A. Zhu published paper on “Achieving Anonymity via Clustering”.

This paper explained Existing clustering approach for local-recoding anonymization mainly concentrate on record linkage attacks mainly under the k-anonymity privacy model, without any importance to privacy breaches incurred by sensitive attribute linkage. Relatively propose a constant factor approximation algorithm for two clustering based anonymization problem, ie, r-GATHER and r- CELLULAR CLUSTERING, here the centers for clusters are published without generalization or suppression. 2.3 SUMMARY Literature survey is most important part of the thesis that helps to improve the analysis and it provides many statistic and strategies were followed by various research persons. It gives multiple angles for a specified technique to analyze the research topic.

In this literate review the concepts are revised and it gives clarity to apply the technique on this research.7 3. PROBLEM DEFINITION In cloud computing, all the user data are stored in the cloud resources. The results are distributed to the user through the network when they needed. Most of the industrial data stored in cloud computing, but cannot predict all stored data must have secured, hence most of cloud data are encrypted.

Even more encryption algorithm invented, sensitive information can leak if that one key is leaked so, less secure. Most of the encryption key is managed by cloud providers, so providers may break all information. This can bring considerable economic loss or severe social reputation impairment to data owners.

As such, sharing or releasing privacy-sensitive data sets to third-parties in cloud will bring about potential privacy concerns, and therefore requires strong privacy preservation. The problems identified from the existing approaches are analyzed for privacy preserving and scalability. Some importance will gave to the local recoding technique for the record linkage attacks over the data sets.

8 4. METHODOLOGY This system is mainly concentrated on anonymization method with is used to provide privacy to the dataset so that the attacker will not gain any sensitive information about the individuals. Anonymization is the best method to provide privacy when compared to the other methods like randomization, perturbation etc. Anonymization can be done in many ways, there are several tools available to perform anonymization. Health care and financial data are very sensitive.

There are many methods to provide privacy to the dataset. The objective of this system is to run the k-anonymity method. A hospital dataset which contains the patient’s information with attributes of Patient id, Patient Name, Age, Sex and disease as shown in table 1. In this table, Name attribute is the personal identification, Disease is the sensitive attribute. If suppose we want to provide the privacy of the data set, the patient consultancy field of a table is removed and it will be modified to another table as follows. Name Patient Id Age Sex Disease Alice 47677 29 M Ovarian Cancer Boby 47678 22 M Ovarian Cancer Peter 47602 27 M Prostate Cancer Emelee 47909 43 M Flu Holdon 47905 32 F Heart Disease Cloyce 47906 47 M Heart Disease Table 1: Patient dataset.9 Zipcode Age Sex Disease 47677 29 M Ovarian Cancer 47678 22 M Ovarian Cancer 47602 27 M Prostate Cancer 47909 43 M Flu 47905 32 F Heart Disease 47906 47 M Heart Disease Table 2: Patient dataset after removing Name attribute So removing the personal identification information will not provide complete privacy to the data.

To provide privacy to the dataset first we have to remove the personal Identification information and then we have to anonymize the quasi identifiers. The sensitive attributes should always be released directly because researcher’s want this information. Different privacy preserving methods have been proposed. To anonymize the quasi-identifiers, K-anonymity. 4.1 K-ANONYMITY This approach is as follows: The information for each person contained in the released dataset cannot be distinguished from at least k-1 individuals whose information also appears in the data. For example: if an attacker with the only information of birthdates and gender is trying to identify a person in the released dataset. There are k persons in the table with the same birth date and gender.

In k anonymity any quasi-identifier present in the released table must appear in at least k records. The goal of K-anonymity is to make each record indistinguishable from at least k-1 other records. These K records form an equivalence class.10 K-anonymity uses generalization and suppression. Using generalization, k anonymity replaces specific quasi-identifiers with less specific values until it gets K identical values. And it uses suppression when generalization causes too much information loss, which is referred as outliers. Form the table 1 we have 3 quasi-identifiers which can be generalized as shown in the figure 1 Figure 1: Generalization on Quasi-identifiers like patient id, age and sex By applying k=2 anonymity and quasi-identifier { patient id , Age, sex} on table 2 we will get the new table 3. Now if we compare table 2 and table 3 it is difficult for an outsider to find the sensitive information because there are three people with generalized patient id and age.

In table 3 first three records form one equivalence class and last two records are another equivalence class. Table 3: k-anonymity on table 2 Zipcode Age Sex Disease 476** 2* M OvarianCancer 476** 2* M OvarianCancer 476** 2* M ProstateCancer 479** 3* F HeartDisease 479** 4* M Flu 479** 4* M HeartDisease11 Any records which has not come into any equivalence class should be suppressed. In this table record 4 will not fall into any of the equivalence class so it should be suppressed.

By applying the generalization and suppression to all fields on table 3 it results to another Table 5. table 4 : Generalization and suppression The problem with the k-anonymity is, it will not provide privacy if sensitive values in an equivalence class lack diversity and also if the attacker has background knowledge. Consider Table 4 the first 3 records which form an equivalence class have the same sensitive attribute values where there is no privacy and attacker can direct to gain the information. And last three records if attacker has some background knowledge about the person (ex. The person father is a heart patient) then this information may be useful for the attacker to gain the sensitive information. 4.2 Triple DES Algorithm Triple DES is another mode of DES operation.

It takes three 64-bit keys, for an overall key length of 192 bits. In Stealth, you simply type in the entire 192-bit (24 character) key rather than entering each of the three keys individually. The Triple DES DLL then breaks the user-provided key into three sub keys, padding the keys if necessary so they are each 64 bits long. Zipcode Age Sex Disease 476** 2* M OvarianCancer 476** 2* M OvarianCancer 476** 2* M ProstateCancer * * * * 479** 4* M Flu 479** 4* M Heart Disease Equivalence Class Equivalence Class Suppressed Record12 The procedure for encryption is exactly the same as regular DES, but it is repeated three times, hence the name Triple DES.

The data is encrypted with the first key, decrypted with the second key, and finally encrypted again with the third key. Triple DES runs three times slower than DES, but is much more secure if used properly. The procedure for decrypting something is the same as the procedure for encryption, except it is executed in reverse. Like DES, data is encrypted and decrypted in 64-bit chunks. Although the input key for DES is 64 bits long, the actual key used by DES is only 56 bits in length. The least significant (right-most) bit in each byte is a parity bit, and should be set so that there are always an odd number of 1s in every byte.

These parity bits are ignored, so only the seven most significant bits of each byte are used, resulting in a key length of 56 bits. This means that the effective key strength for Triple DES is actually 168 bits because each of the three keys contains 8 parity bits that are not used during the encryption process. The process of encryption is as follows – 1. Encrypt the data using DES Algorithm with the help of first key. 2.

Now, decrypt the output generated from the first step using DES Algorithm with the help of second key. 3. Finally, encrypt the output of second step using DES Algorithm with the help of third key. The decryption process of any cipher text that was encrypted using Triple DES Algorithm is the reverse of the encryption process i.e., 1. Decrypt the cipher text using DES Algorithm with the help of third key.

2. Now, encrypt the output generated from the first step using the DES Algorithm with the help of second key. 3. Finally, decrypt the output of the second step using DES Algorithm with the help of first key. The process of encrypt – decrypt – encrypt help complexing things and securing the data.

The three keys can also be same or two of them can be same. But it is recommended to use all the three keys different.13 4.3 SYSTEM SPECIFICATION Hardware Specification Processor : Intel Pentium i3. RAM : 4GB Hard drive : 500 GB Monitor : 17″ Flat L.G color SVGA Keyboard : Multimedia keyboard Mouse : Optical scroll mouse Software Specification Operating System : Windows XP and Above Front-End : ASP.

Net 2010 Database Server : Microsoft SQL Server Application Server : IIS14 4.4 SOFTWARE DESCRIPTION ASP.NET ASP.NET is more than the next version of Active Server Pages (ASP), it is a unified web development platform that provides the services necessary for developers to build enterprise-class web applications. While ASP.

NET is largely syntax compatible with ASP, it also provides a new programming model and infrastructure for more secure, scalable, and stable applications. User can feel free to augment user existing ASP applications by incrementally adding ASP.NET functionality to them.

ASP.NET is a compiled, NET-based environment; user can author applications in any .NET compatible language, including Visual Basic .NET, C#, and JScript .NET.

Additionally, the entire .NET Framework is available to any ASP.NET application runtime environment, type safety, inheritance, and so on. ASP.NET has been designed to work seamlessly with WYSIWYG (What you see is what you get) HTML editors and other programming tools, including Microsoft Visual Studio .NET.

Not only does this make web development easier, but it also provides all the benefits that these tools have to offer, including a GUI that developers can use to drop server controls onto a web page and fully integrated debugging support. Developers can choose from the following two features when creating an ASP.NET application, web Forms and web services, or combine these in any way they see fit. Each is supported by the same infrastructure that allows user to use authentication schemes, cache frequently used data, or customize user application’s configuration, to name only a few possibilities.15 ADO.NET ADO.NET provides consistent access to data sources such as Microsoft Access, as well as data sources exposed via OLEDB.

Data sharing consumer applications can use ADO.NET to connect to these data sources and retrieve, manipulate and update data. ADO.NET cleanly factors data access from data manipulation into discrete component that can be separately or in random. ADO.NET includes .NET data providers for connecting to the database, executing commands, and retrieving results.

Features of ASP.NET ? Web forms allows user to build powerful forms based web pages. When building these pages, user can use ASP.NET server controls to create common GUI elements and program them for common tasks. ? Using web services, business can expose programmatic interfaces to their data or business logic which in turn can be obtained and manipulated by client-server or server-server scenarios.

? If users have ASP development skills, the ASP.NET programming model will be seem very familiar to user however the ASP.NET object model has changed significantly from ASP, making it more structured and object-oriented.

? ASP.NET provides easy-to-use application and session – state facilities that are familiar to ASP developers. ? ASP.NET code is compiled, rather than interpreted, which allow early binding, strong typing and just-in-time (JIT) compilation to native code to name only a few of its benefits.16 SQL Server Microsoft SQL Server is a relational database server, developed by Microsoft.

It is a software product whose primary function is to store and retrieve data as requested by other software applications, be it those on the same computer or those running on another computer across a network (including the Internet). There are at least a dozen different editions of Microsoft SQL Server aimed at different audiences and for different workloads (ranging from small applications that store and retrieve data on the same computer, to millions of users and computers that access huge amounts of data from the Internet at the same time). Microsoft SQL Server is an application used to create computer databases for the Microsoft Windows family of server operating systems. Microsoft SQL Server provides an environment used to generate databases that can be accessed from workstations, the Internet, or other media such as a personal digital assistant (PDA).

Whenever a query is submitted to SQL Server, the SQL engine must make decisions about how to go about retrieving the data for the user. Inside the SQL Server query processing engine, there is a section of code called the query optimizer whose function is to find the most efficient means of retrieving data at that particular time. This query optimizer compares different possible methods of retrieving the data (called execution plans) and then chooses one.

Once this is done, the query engine goes about using this plan to retrieve the data requested by the query. In any database system, returning data to the client must be done as efficiently and quickly as possible to minimize contention. If the database server spends an inordinate amount of time processing one query, the performance of other queries will suffer. In order for the server to find the most efficient method of satisfying the query, it must spend resources examining the query and comparing different methods of retrieving the data.

This overhead, however, is often returned to the user in overall time savings when the most efficient method of satisfying the query is chosen. This is similar to climbing an unfamiliar mountain. There are different types of query optimizers used in various relational database management systems. Microsoft SQL Server uses a “cost-based” query optimizer in determining which of the various methods of retrieving data it will pick and send to the query engine.

A cost-based optimizer assigns a cost to each method of retrieving data based on the resources required17 to process the query. Processor time, disk I/O, etc. are all assigned costs based on the number of rows that must be examined in a particular operation. Once the optimizer has assigned the costs, it sums up the total cost for each execution plan that was investigated. Based on the design of the system, the query optimizer chooses an execution plan, which is then sent to the query engine for processing. SQL Server does not always choose the execution plan with the lowest total resource cost as one might expect. Instead, SQL Server is designed to pick the execution plan that is reasonably close to the theoretical minimum and will return results to the client as quickly as possible with a reasonable cost.

The definition of reasonable will change as conditions within the SQL Server and the load changes. This results in a dynamic, efficient query optimizer that delivers some of the best performance in the industry.18 5. STRUCTURAL DESIGN 5.1 INPUT DESIGN The input to the system was designed So that the required information can be collected and corrected quickly.

The data collected are entered into the system through input screens, when a data is to be entered the description of the data is displayed at the bottom of the screen. Input design is given through selection-based links. The input design is the process of converting an external user oriented description of the input to a system in to a machine-oriented format.

Data processing involves the usage of accurate data. Errors entered by the data entry operation can be controlled by the input design. The goal of designing input data is to make data entry an easy operation. An efficient input designing will avoid the frequent occurrence of errors. ? To provide a cost effective method of input. ? To achieve the highest possible level of accuracy. ? To ensure that the input is acceptable to and understood by the user.

In this system following input screens are designed to get user’s information. ? Authentication ? Patient Details ? Staff details ? Privacy details ? Staff registration ? Patient Registration Authentication Authentication screen provide the security to the system. It get username and password from the users. Patient Details Patient details screen is used to get the patient details and it includes the details of patient id, name, address, contact, email. Admin can enter the details of the patient and admin only have permission to add, edit and delete permissions.19 Staff Details Staff details screen is used to get the staff details and it includes the details of staff id, name, address, contact, email and department. Admin can enter the details of the staff and admin only have permission to add, edit and delete permissions.

Privacy Details Privacy details screen get sensitive information from the admin. In this screen admin can enter privacy data of the patient. This screen includes the details of the patient id, consultation date and medical history.

These details are encrypted on this screen. Staff Registration Staff registration screen allow staff to register on this site. In this screen staff can enter staff id, it will show staff name and it get the username and password from the staff. Patient Registration Patient registration screen allow Patient to register on this site. In this screen patient can enter patient id, it will show patient name and it get the username and password from the patient. 5.2 OUTPUT DESIGN The ultimate goal of the development of the system is to produce effective outputs.

In output design, it is determined how the information is to be displayed for immediate need. It is the most important and direct source of information to the user. Efficient and intelligent output design improves the system’s relationships with the user and help in decision making. This system produces following reports. ? Patient Details ? Privacy Details Patient Details Patient details screen is used to display the patient details and it includes the details of patient id, name, address, contact, email.

All users can see this report. Privacy Details Privacy details screen shows the sensitive information. In this screen admin and user can view privacy data and their medical history. These details are decrypted and download by the patient.20 5.3 DATABASE DESIGN Database design is the process of producing a detailed data model of a database. This data model contains all the needed logical and physical design choices and physical storage parameters needed to generate a design in a data definition language, which can then be used to create a database. A fully attributed data model contains detailed attributes for each entity.

The term database design can be used to describe many different parts of the design of an overall database system. Principally, and most correctly, it can be thought of as the logical design of the base data structures used to store the data. In the relational model these are the tables and views. In an object database the entities and relationships map directly to object classes and named relationships. However, the term database design could also be used to apply to the overall process of designing, not just the base data structures, but also the forms and queries used as part of the overall database application within the database management system (DBMS).

The process of doing database design generally consists of a number of steps which will be carried out by the database designer. Usually, the designer must: ? Determine the data to be stored in the database. ? Determine the relationships between the different data elements. ? Superimpose a logical structure upon the data on the basis of these relationships. Table Name : Doctor Primary Key : Dcode Field Name Data Type Size Description Dcode Varchar 5 Doctor Code Dname Varchar 30 Doctor name Special Varchar 50 Specialization Cont Varchar 15 Contact Email Varchar 30 Email21 Table Name : Patient Primary Key : Patid Field Name Data Type Size Description Patid Varchar 5 Patient Id Pname Varchar 30 Patient name Dob DateTime 8 Date of Birth Gender Varchar 7 Gender Cont Varchar 15 Contact Addr Varchar 150 Address Email Varchar 30 Email Id Table Name : Staff Primary Key : staffed Field Name Data Type Width Description StaffId Varchar 5 Staff Id Sname Varchar 30 Staff name Desi Varchar 30 Designation Cont Varchar 15 Contact Email Varchar 30 Email Id Table Name : regtable Field Name Data Type Width Description Uname Varchar 30 Username Pwd Varchar 30 Password Utype Varchar 30 User Type(Staff or patient) Uid Varchar 5 User Id22 Table Name : metadata Reference Key : Patid Field Name Data Type Width Description Patid Varchar 5 Patient Id Condate DateTime 8 Consulting date Dcode Varchar 5 Doctor code Condet Varchar 300 Consultation details 5.4 Entity Relationship Diagram Patient PatientId pname Doctor Dcode Dname metadata PatientId Dcode Admin Dcode23 5.5 Data Flow Diagram Product Maintenance Stores Admin Meta Data User Registration Stores Patient Register Doctor View24 6.

RESULTS AND DISCUSSION A triple DES algorithm is followed in the proposed system. Since a three level security mechanism is used to encrypt the patient diagnostics file. The encrypted file is uploaded successfully to the cloud. From the cloud, patient receiver can download the data which is the form of cipher text. Each patient then decrypts the cipher text to the original data.

Any number of patient, doctor and staff can be registered first. Then all the register details are stored in the cloud. Once the user registered, each time they can access the details based on id. Only the registered users can access the file, otherwise they are not allowed to access site. K-anonymity algorithm is used to protect the privacy information. Fig 6.

1 Upload Privacy File25 Fig 6.2 Download Privacy File26 7. CONCLUSION AND FUTURE WORK Privacy is very important to protect the sensitive data from the attacker. To provide privacy to the data anonymization methods can be used. In this system is done by using K-anonymity method and Triple DES algorithm using .net.

These techniques are applied in the hospital domain and its works efficiently and secure patient data are shared with in the hospital environment. In Future the system will add advance security techniques to used patient details.27 8. REFERENCES 1 Dr.Kumar saurah, “cloud computing”, Wiley india pvt Ltd,First Edition. 2 J. Xu, W.

Wang, J. Pei, X. Wang, B. Shi, and A. W.

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12 Zhang X, Liu C, Nepal S, Pandey S, Chen J. ” A Privacy Leakage Upper Bound Constraint-Based Approach for Cost-Effective Privacy Preserving of Intermediate Data sets in Cloud”, IEEE VOL. 24, NO. 6, JUNE 2013 13 G. Aggarwal, R.

Panigrahy, T. Feder, D. Thomas, K. Kenthapadi, S. Khuller, and A. Zhu, “Achieving Anonymity via Clustering”, ACM Transactions on Algorithms June 2010 DOI:10.1145 14 Abraham Silbarschatz, “Database System Concepts”, Tata MC-Graw Hill Companies, Third Edition.

15 Chutney Heber, “ASP.Net”, Addison Wesley Publications, January 1996. 16 Donfox, “Pure ASP.Net”, BPB Publications, First Edition. 17 David Soloman, “Sams Teach Yourself Asp.

Net in 21 days”, Crimpson Publications, Second Edition March 2001. 18 William Stallings, “Cryptography and Network Security”, Pearson Publication, Fourth Edition, 19 Elias. M. Award, “System Analysis and Design”, Golgatia Publications, Second Edition. Websites ? ? www.w3schools.com29


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