Oruta is a privacy-preserving public auditing mechanism for shared data in the cloud that: 1) Utilizes ring signatures to construct homomorphic authenticators, allowing a third party auditor to verify the integrity of shared data without retrieving the entire data while keeping the identity of the signer private. 2) Extends the mechanism to support batch auditing, enabling the verification of multiple shared data simultaneously in a single auditing task. 3) Continues to use random masking to support data privacy during public auditing and leverages index hash tables to support fully dynamic operations on shared data.

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CHAPTER 1
INTRODUCTION
Cloud service providers manage an enterprise-class infrastructure that offers a scalable,
secure and reliable environment for users, at a much lower marginal cost due to the sharing
nature of resources. It is routine for users to use cloud storage services to share data with
others in a team, as data sharing becomes a standard feature in most cloud storage offerings,
including Drop box and Google Docs.
The integrity of data in cloud storage, however, is subject to scepticism and scrutiny, as
data stored in an untrusted cloud can easily be lost or corrupted, due to hardware failures and
human errors. To protect the integrity of cloud data, it is best to perform public auditing by
introducing a third party auditor (TPA), who offers its auditing service with more powerful
computation and communication abilities than regular users.
The first provable data possession (PDP) mechanism to perform public auditing is
designed to check the correctness of data stored in an untrusted server, without retrieving the
entire data. Moving a step forward, Wang et al. (referred to as WWRL) is designed to
construct a public auditing mechanism for cloud data, so that during public auditing, the
content of private data belonging to a personal user is not disclosed to the third party auditor.
We believe that sharing data among multiple users is perhaps one of the most engaging
features that motivates cloud storage. A unique problem introduced during the process of
public auditing for shared data in the cloud is how to preserve identity privacy from the TPA,
because the identities of signers on shared data may indicate that a particular user in the
group or a special block in shared data is a higher valuable target than others. For example,
Alice and Bob work together as a group and share a file in the cloud. The shared file is
divided into a number of small blocks, which are independently signed by users. Once a
block in this shared file is modified by a user, this user needs to sign the new block using her
public/private key pair. The TPA needs to know the identity of the signer on each block in
this shared file, so that it is able to audit the integrity of the whole file based on requests from
Alice or Bob.
We propose Oruta, a new privacy preserving public auditing mechanism for shared
data in an untrusted cloud. In Oruta, we utilize ring signatures to construct homomorphic

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authenticators, so that the third party auditor is able to verify the integrity of shared data for a
group of users without retrieving the entire data — while the identity of the signer on each
block in shared data is kept private from the TPA. In addition, we further extend our
mechanism to support batch auditing, which can audit multiple shared data simultaneously in
a single auditing task. Meanwhile, Oruta continues to use random masking to support data
privacy during public auditing, and leverage index hash tables to support fully dynamic
operations on shared data. A dynamic operation indicates an insert, delete or update operation
on a single block in shared data. A high-level comparison between Oruta and existing
mechanisms in the literature is shown. To our best knowledge, this represents the first attempt
towards designing an effective privacy preserving public auditing mechanism for shared data
in the cloud.
1.1 EXISTING SYSTEM
Many mechanisms have been proposed to allow not only a data owner itself but also
a public verifier to efficiently perform integrity checking without downloading the entire data
from the cloud, which is referred to as public auditing . In these mechanisms, data is divided
into many small blocks, where each block is independently signed by the owner; and a
random combination of all the blocks instead of the whole data is retrieved during integrity
checking . A public verifier could be a data user (e.g., researcher) who would like to utilize
the owner’s data via the cloud or a third-party auditor (TPA) who can provide expert integrity
checking services.
Moving a step forward, Wang et al. designed an advanced auditing mechanism .so
that during public auditing on cloud data, the content of private data belonging to a personal
user is not disclosed to any public verifiers. Unfortunately, current public auditing solutions
mentioned above only focus on personal data in the cloud .We believe that sharing data
among multiple users is perhaps one of the most engaging features that motivates cloud
storage. Therefore, it is also necessary to ensure the integrity of shared data in the cloud is
correct.
Existing public auditing mechanisms can actually be extended to verify shared data
integrity. However, a new significant privacy issue introduced in the case of shared data with
the use of existing mechanisms is the leakage of identity privacy to public verifiers. However,
a new significant privacy issue introduced in the case of shared data with the use of existing
mechanisms is the leakage of identity privacy to public verifiers.

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DISADVANTAGES OF EXISTING SYSTEM:
 Failing to preserve identity privacy on shared data during public auditing will reveal
significant confidential information to public verifiers.
 Protect these confidential information is essential and critical to preserve identity
privacy from public verifiers during public auditing.
1.2 PROPOSED SYSTEM:
To solve the above privacy issue on shared data, we propose Oruta, a novel privacy-
preserving public auditing mechanism. More specifically, we utilize ring signatures to
construct homomorphic authenticators in Oruta, so that a public verifier is able to verify the
integrity of shared data without retrieving the entire data while the identity of the signer on
each block in shared data is kept private from the public verifier.
In addition, we further extend our mechanism to support batch auditing, which can
perform multiple auditing tasks simultaneously and improve the efficiency of verification for
multiple auditing tasks. Meanwhile, Oruta is compatible with random masking, which has
been utilized in WWRL and can preserve data privacy from public verifiers. Moreover, we
also leverage index hash tables from a previous public auditing solution to support dynamic
data. A high-level comparison among Oruta and existing mechanisms is presented.
ADVANTAGES OF PROPOSED SYSTEM:
 A public verifier is able to correctly verify shared data integrity.
 A public verifier cannot distinguish the identity of the signer on each block in shared
data during the process of auditing.
 The ring signatures generated for not only able to preserve identity privacy but also able
to support blockless verifiability.

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CHAPTER 2
LITERATURE SURVEY
Literature survey is the most important step in software development process. Use of
privacy-preserving public auditing mechanism, and cryptographic scheme can increase the
security level for the data that are stored on the cloud servers.
Following is the literature survey of some existing technique for cloud security
[1] Q Wang, C. Wang, “Enabling Public Verifiability and Data Dynamics for Storage
Security in Cloud Computing”.
Cloud Computing has been envisioned as the next generation architecture of IT
Enterprise. It moves the application software and databases to the centralized large data
centers, where the management of the data and services may not be fully trustworthy. This
unique paradigm brings about many new security challenges, which have not been well
understood. This work studies the problem of ensuring the integrity of data storage in Cloud
Computing. In particular, we consider the task of allowing a third party auditor (TPA), on
behalf of the cloud client, to verify the integrity of the dynamic data stored in the cloud. The
introduction of TPA eliminates the involvement of the client through the auditing of whether
his data stored in the cloud is indeed intact, which can be important in achieving economies
of scale for Cloud Computing. The support for data dynamics via the most general forms of
data operation, such as block modification, insertion and deletion, is also a significant step
toward practicality, since services in Cloud Computing are not limited to archive or backup
data only. While prior works on ensuring remote data integrity often lacks the support of
either public verifiability or dynamic data operations, this paper achieves both.
We first identify the difficulties and potential security problems of direct extensions
with fully dynamic data updates from prior works and then show how to construct an elegant
verification scheme for the seamless integration of these two salient features in our protocol
design. In particular, to achieve efficient data dynamics, we can improve the existing proof of
storage models by manipulating the classic (MHT) Merkle Hash Tree construction for block
tag authentication. To support efficient handling of multiple auditing tasks, we can further
explore the technique of bilinear aggregate signature to extend our main result into a multi-
user setting, where TPA can perform multiple auditing tasks simultaneously. Extensive
security and performance analysis show that the proposed schemes are highly efficient and
provably secure.

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[2] C. Wang, Q. Wang, “Privacy-Preserving Public Auditing for Data Storage Security
in Cloud Storage”.
Using Cloud Storage, users can remotely store their data and enjoy the on-demand
high quality applications and services from a shared pool of configurable computing
resources, without the burden of local data storage and maintenance. However, the fact that
users no longer have physical possession of the outsourced data makes the data integrity
protection in Cloud Computing a formidable task, especially for users with constrained
computing resources. In this paper, author propose a secure cloud storage system supporting
privacy-preserving public auditing .We can further extend our result to enable the TPA to
perform audits for multiple users simultaneously and efficiently.
[3] A. Juels and B. S. Kaliski, “PORs: Proofs of Retrievability for Large Files”.
We define and explore proofs of retrievability (POR). A POR scheme enables an
archive or backup service (prover) to produce a concise proof that a user (verifier) can
retrieve a target file F, that is, that the archive retains and reliably transmits file data sufficient
for the user to recover F in its entirety. A POR may be viewed as a kind of cryptographic
proof of knowledge (POK), but one specially designed to handle a large file (or bit string) F.
We explore POR protocols here in which the communication costs, number of memory
accesses for the prover, and storage requirements of the user (verifier) are small parameters
essentially independent of the length of F. In addition to proposing new, practical POR
constructions, we explore implementation considerations and optimizations that bear on
previously explored, related schemes. In a POR, unlike a POK, neither the prover nor the
verifier need actually have knowledge of F. PORs give rise to a new and unusual security
definition whose formulation is another contribution of our work.
We view PORs as an important tool for semi-trusted online archives. Existing
cryptographic techniques help users ensure the privacy and integrity of files they retrieve. It
is also natural, however, for users to want to verify that archives do not delete or modify files
prior to retrieval. The goal of a POR is to accomplish these checks without users having to
download the files themselves. A POR can also provide quality-of service guarantees, i.e.,
show that a file is retrievable within a certain time bound.
[4] G. Ateniese, R. D. Pietro, “Scalable and Efficient Provable Data Possession”.
In this paper author introduce a model for provable datapossession (PDP) that allows
a client that has stored data at an untrusted server to verify that the server possesses the
original data without retrieving it. The model generates probabilistic proofs of possession by

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sampling random sets of blocks from the server, which drastically reduces I/O costs. The
client maintains a constant amount of metadata to verify the proof. The challenge/response
protocol transmits a small, constant amount of data, which minimizes network
communication. Thus, the PDP model for remote data checking supports large data sets in
widely distributed storage systems. To support the dynamic auditing, Ateniese et al.
developed a dynamic provable data possession protocol based on cryptographic hash function
and symmetric key encryption. Their idea is to pre compute a certain number of metadata
during the setup period, so that the number of updates and challenges is limited and fixed
beforehand.
The author construct a highly efficient and provably secure PDP technique based
entirely on symmetric key cryptography, while not requiring any bulk encryption. Also, in
contrast with its predecessors, this PDP technique allows outsourcing of dynamic data, i.e, it
efficiently supports operations, such as block modification, deletion and append.
[5] C. Wang, Q. Wang, “Towards Secure and Dependable Storage Services in Cloud
Computing”.
In this paper, author has propose an effective and flexible distributed storage
verification scheme with explicit dynamic data support to ensure the correctness and
availability of users’ data in the cloud. They rely on erasure correcting code in the file
distribution preparation to provide redundancies and guarantee the data dependability against
Byzantine servers, where a storage server may fail in arbitrary ways. This construction
drastically reduces the communication and storage overhead as compared to the traditional
replication based file distribution techniques. By utilizing the homomorphic token with
distributed verification of erasure coded data, their scheme achieves the storage correctness
insurance as well as data error localization: whenever data corruption has been detected
during the storage correctness verification, this scheme can almost guarantee the
simultaneous localization of data errors, i.e., the identification of the misbehaving server(s).
In order to strike a good balance between error resilience and data dynamics, their
work further explore the algebraic property of our token computation and erasure coded data,
and demonstrate how to efficiently support dynamic operation on data blocks, while
maintaining the same level of storage correctness assurance. In order to save the time, re-
sources, and even the related online burden of users, extension of the proposed main scheme
to support third party auditing, where users can safely delegate the integrity checking tasks to
third party auditors and be worry free to use the cloud storage services.

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[6] G. Ateniese, R. D. Pietro, “MAC Based Solution”.
It is used to authenticate the data. In this, user upload data blocks and MAC to CS provide its
secret key SK to TPA. The TPA will randomly retrieve data blocks & Mac uses secret key to
check correctness of stored data on the cloud. Problems with this system are listed below as
 It introduces additional online burden to users due to limited use (i.e. Bounded usage)
and Stateful verification.
 Communication & computation complexity
 TPA requires knowledge of data blocks for verification
 Limitation on data files to be audited as secret keys are fixed
 After usages of all possible secret keys, the user has to download all the data to
recomputed
MAC & republish it on CS.
 TPA should maintain & update states for TPA which is very difficult
 It supports only for static data not for dynamic data.
[7] A. Juels and B. S. Kaliski, “HLA Based Solution”.
It supports efficient public auditing without retrieving data block. It is aggregated and
required constant bandwidth. It is possible to compute an aggregate HLA which authenticates
a linear combination of the individual data blocks.
[8] N. Cao, S. Yu, S. Yang, “Using Virtual Machine”.
They proposed Virtual machines which uses RSA algorithm, for client data/file
encryption and decryptions. It also uses SHA 512 algorithm which makes message digest and
check the data integrity. The Digital signature is used as an identity measure for client or data
owner. It solves the problem of integrity, unauthorized access, privacy and consistency.
[9] C. Erway, A. Kupcu, “Non Linear Authentication”.
They suggested Homomorphic non linear authenticator with random masking
techniques to achieve cloud security. K. Gonvinda proposed digital signature method to
protect the privacy and integrity of data. It uses RSA algorithm for encryption and decryption
which follows the process of digital signatures for message authentication.
[10] S. Marium, “Using EAP”.
S. Marium proposed use of Extensible authentication protocol (EAP) through three
ways hand shake with RSA. They proposed identity based signature for hierarchical

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architecture. They provide an authentication protocol for cloud computing (APCC) . APCC is
more lightweight and efficient as compared to SSL authentication protocol. In this, Challenge
–handshake authentication protocol (CHAP) is used for authentication. When make request
for any data or any service on the cloud. The Service provider authenticator (SPA) sends the
first request for client identity.
The steps are as follows
 When Client request for any service to cloud service provider, SPA send a CHAP
request / challenge to the client.
 The Client sends CHAP response/ challenges which is calculated by using a hash
function to SPA
 SPA checks the challenge value with its own calculated value. If they are matched
then SPA sends CHAP success message to the client.
Implementation of this EAP-CHAP in cloud computing provides authentication of the client.
It provides security against spoofing identity theft, data tempering threat and DoSattack. The
data is being transferred between client and cloud providers. To provide security, asymmetric
key encryption (RSA) algorithm is used. Dhiyanesh proposed Mac based and signature based
schemes for realizing data audit ability and during auditing phase data owner provides a
secret key to cloud server and ask for a MAC key for verification. Wang proposed an
effective and flexible distributed schemes as Homomorphic token with distributed
verification of erasure coded data proposed scheme achieves anintegration of storage
correctness insurance and data error localization i.e. identification of misbehaving server.
[11] K. B.Wang, “Random Masking Technique”.
K. B. Wang proposed privacy preserving Third party auditing without data
encryption. It uses a linear combination of sampled block in the server’s response is masked
with randomly generated by a pseudo random function (PRF) .Researchers of ―Investigation
of TPA for Cloud Data Security use the concept of virtual machines, The RSA algorithm is
used to encode and decode the data and SHA 512 algorithm is used for message digest which
check the integrity of information Dr. P.K. Deshmukh uses the new password at each
instance which will be transferred to the mail server for each request to obtain data security
and data integrity of cloud computing .
This protocol is secure against an untrusted server as well as third party auditor.
Client as well as trusted third party verifier should be able to detect the changes done by the
third party auditor. The client data should be kept private against third party verifier. It

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supports public verifiability without help of a third party auditor. This protocol does not leak
any information to the third party verifier to obtain data security.
This proposed protocol is secure against the untrusted server and private against third
party verifier and support data dynamics. In this system, the password is generated and that
will be transferred to email address of the client. Every time a key is used to perform various
operations such as insert, update delete on cloud data. It uses time based UUID algorithm for
key generation based on pseudo random numbers. If an intruder tries to access the users’ data
on a cloud, that IP address will be caught and transferred to the user so that user will be aware
of.
[12] C. Wang, “Analysis of protocol proposed by C. Wang which contains security
flaws”.
Researchers of ―Cryptanalysis of Auditing protocol proposed by Wang for data
storage security in cloud computing‖ analyses the Protocol proposed by Wang et al and find
security flaws in their protocol. A Publicuditing protocol is a collection of 4 polynomial time
algorithm as (Keygen, TagBlock, Genproof, and CheckProof)
 Keygen: User executes Keygen for key generation.
 TagBlock: User executes TagBlock to produce verification metadata.
 Genproof: Cloud server executes Genproof for proof of possession.
 CheckProof: TPA will validate a proof of possession by executing CheckProof.
The Problem with this system is that cloud server might be malicious which might not keep
data or might delete the data owned by cloud users and might even hide the data possessions.
 Data modification tag forging attacks
 Data lost auditing pass attack
 Data interception and modification attack
 Data Eavesdropping and Forgery
This protocol is vulnerable to existential forgeries known as message attack from a
malicious cloud server and an outside attacker. The analysis shows that they are not
providing any security for cloud data storage.

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CHAPTER 3
SYSTEM REQUIREMENTS SPECIFICATION
Software requirement specification is a fundamental document, which forms the
foundation of the software development process. It not only lists the requirements of a
system but also has a description of its major feature. An SRS is basically an organization’s
understanding of a customer or potential client’s system requirements and dependencies at a
particular point in time prior to any actual design or development work. It’s a two way
insurance policy that assures that both the client and the organization understand the other’s
requirements from that perspective at a given point of time. The SRS also functions as a
blueprint for completing a project with as little cost growth as possible. The SRS is often
referred to as the ― parent‖ document because all subsequent project management documents,
such as design specifications, statements of work, software architecture specifications, testing
and validation plans, and documentation plans, are related to it. It is important to note that
and SRS contains functional and non-functional requirements only; it doesn’t offer design
suggestions, possible solutions to technology or business issues, or any other information
other than what the development team understands the customer’s system requirements to be.
3.1 Requirements
3.1.1 Functional Requirements
Functional requirements characterize a component of a software framework and how
the framework must carry on when given particular inputs or conditions. These may
incorporate estimations, data manipulation and handling and other particular functionality.
The details of our public auditing mechanism in Oruta includes: Key-Gen, Sig-Gen,
Modify, Proof-Gen and Proof Verify. In Key-Gen, users generate their own public/private
key pairs. In Sig-Gen, a user is able to compute ring signatures on blocks in shared data. Each
user is able to perform an insert, delete or update operation on a block, and compute the new
ring signature on this new block in Modify. Proof Gen is operated by the TPA and the cloud
server together to generate a proof of possession of shared data. In Proof-Verify, the TPA
verifies the proof and sends an auditing report to the user.

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 Functional Requirement 1
Description: Owner registration.
Input: Name, Gender, DOB and E-mail.
Processing: Validity of the owner is verified.
Output: A valid registration is completed successfully.
 Functional Requirement 2
Description: Owner Login.
Input: User name and Password.
Processing: validity of the provided user name and password is verified.
Output: A valid owner/user is granted access.
 Functional Requirement 3
Description: TPA Registration and Login.
Input: Required information with user name and password.
Processing: Validates the authenticity of the presented data.
Output: Valid Registration is allowed access to the cloud.
 Functional Requirement 4
Description: File Upload and Verification.
Input: Name of the file to be uploaded.
Processing: The mentioned file is being uploaded onto the cloud.
Output: The said file is successfully uploaded onto the cloud.
 Functional Requirement 5
Description: File Modification.
Input: The uploaded file.
Processing: The uploaded file is being modified by TPA.
Output: The uploaded file is modified.
3.1.2 Non-Functional Requirements
Non-functional requirements are the prerequisites which are not concerned with the
particular function conveyed by the framework. They determine the criteria that can be
utilized to judge the operation of a framework instead of particular behaviours. They may
identify with emergent framework properties, for example, reliability, response time. Non-
functional requirements emerge through the client needs, as a result of budget constraints,

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organizational policies, and the requirement for interoperability with other software and
hardware frameworks.
 Usability
The framework is simple so that individuals like to utilize it. The client must be
acquainted with the user interfaces and ought not to have issues in relocating to
another framework with another environment. The menus, catches and dialog boxes
are named in a way that they give clear comprehension of the functionality.
 Reliability
The framework is reliable and trustworthy in giving the functionalities. When a user
rolls out a few improvements, the progressions are made visible by the framework.
 Security
As innovation started to develop in quick rate the security turned into the significant
concern of an association. Application will be admissible to be utilized just as a part
of secure system so there is less plausibility of insecurity over the functionality of the
application.
 Adaptability
Adaptability is the capacity of a framework to adjust to varying situations and
circumstances, and to adapt to changes to business approaches and rules. An
adaptable framework is one that easy to reconfigure.
 Portability
The application is developed in .NET. It would be compact to other operating system
provided Ajax Tool Kit is accessible to the OS.
 Scalability
The framework ought to be scalable enough to include new functionalities at a later
stage. There ought to be a typical channel, which can oblige the new functionalities
3.1.3 System Requirements
Software requirement specification is a fundamental document, which forms the
foundation of the software development process. It not only lists the requirements of a
system but also has a description of its major feature. An SRS is basically an organization’s
understanding of a customer or potential client’s system requirements and dependencies at a
particular point in time prior to any actual design or development work. It’s a two way
insurance policy that assures that both the client and the organization understand the other’s

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requirements from that perspective at a given point of time. The SRS also functions as a
blueprint for completing a project with as little cost growth as possible. The SRS is often
referred to as the ― parent‖ document because all subsequent project management documents,
such as design specifications, statements of work, software architecture specifications, testing
and validation plans, and documentation plans, are related to it. It is important to note that
and SRS contains functional and non-functional requirements only; it doesn’t offer design
suggestions, possible solutions to technology or business issues, or any other information
other than what the development team understands the customer’s system requirements to be.
3.1.4 Hardware Requirements
 Processor – Pentium –IV
 Speed – 1.1 GHz
 RAM – 512 MB(min)
 Hard Disk – 40 GB
 Key Board – Standard Windows Keyboard
 Monitor – LCD/LED
3.1.5 Software Requirements
 Operating system: Windows 7
 Coding Language : ASP .NET
 Data Base : SQL Server 2008 R2
 Tool: VISUAL STUDIO 2010.
3.1.6 Feasibility Analysis
The feasibility of the project is analyzed in this phase and business proposal is put
forth with a very general plan for the project and some cost estimates. During system
analysis the feasibility study of the proposed system is to be carried out. This is to ensure
that the proposed system is not a burden to the company. For feasibility analysis, some
understanding of the major requirements for the system is essential.
3.1.7 Social Analysis
The aspect of study is to check the level of acceptance of the system by the user. This
includes the process of training the user to use the system efficiently. The user must not feel
threatened by the system, instead must accept it as a necessity. The level of acceptance by the

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users solely depends on the methods that are employed to educate the user about the system
and to make him familiar with it. His level of confidence must be raised so that he is also able
to make some constructive criticism, which is welcomed, as he is the final user of the system.
3.1.8 Technical Analysis
This study is carried out to check the technical feasibility, that is, the technical
requirements of the system. Any system developed must not have a high demand on the
available technical resources. This will lead to high demands on the available technical
resources. This will lead to high demands being placed on the client. The developed system
must have a modest requirement, as only minimal or null changes are required for
implementing this system.
3.1.9 Economical Analysis
This study is carried out to check the economic impact that the system will have on
the organization. The amount of fund that the company can pour into the research and
development of the system is limited. The expenditures must be justified. Thus the developed
system as well within the budget and this was achieved because most of the technologies
used are freely available. Only the customized products had to be purchased.

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CHAPTER 4
SYSTEM ANALYSIS
4.1 Problem Statement
4.1.1 System Model
This application involves three parties: the cloud server, the third party auditor (TPA)
and users. There are two types of users in a group: the original user and a number of group
users.
The original user and group users are both members of the group. Group members are
allowed to access and modify shared data created by the original user based on access control
polices. Shared data and its verification information (i.e. signatures) are both stored in the
cloud server. The third party auditor is able to verify the integrity of shared data in the cloud
server on behalf of group members. Our system model includes the cloud server, the third
party auditor and users. The user is responsible for deciding who is able to share her data
before outsourcing data to the cloud. When a user wishes to check the integrity of shared
data, she first sends an auditing request to the TPA. After receiving the auditing request, the
TPA generates an auditing message to the cloud server, and retrieves an auditing proof of
shared data from the cloud server. Then the TPA verifies the correctness of the auditing
proof. Finally, the TPA sends an auditing report to the user based on the result of the
verification.
4.2 Threat Model
4.2.1 Integrity Threats
Two kinds of threats related to the integrity of shared data are possible. First, an
adversary may try to corrupt the integrity of shared data and prevent users from using data
correctly. Second, the cloud service provider may inadvertently corrupt (or even remove) data
in its storage due to hardware failures and human errors. Making matters worse, in order to
avoid this integrity threat the cloud server provider may be reluctant to inform users about
such corruption of data.
4.2.2 Privacy Threats
The identity of the signer on each block in shared data is private and confidential to
the group. During the process of auditing, a semi-trusted TPA, who is only responsible for

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auditing the integrity of shared data, may try to reveal the identity of the signer on each block
in shared data based on verification information. Once the TPA reveals the identity of the
signer on each block, it can easily distinguish a high-value target (a particular user in the
group or a special block in shared data).
4.3 Design Objectives
To enable the TPA efficiently and securely verify shared data for a group of users,
Oruta should be designed to achieve following properties:
(1) Public Auditing: The third party auditor is able to publicly verify the integrity of shared
data for a group of users without retrieving the entire data.
(2) Correctness: The third party auditor is able to correctly detect whether there is any
corrupted block in shared data.
(3) Unforgeability: Only a user in the group can generate valid verification information on
shared data.
(4) Identity Privacy: During auditing, the TPA cannot distinguish the identity of the signer
on each block in shared data.

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CHAPTER 5
SYSTEM DESIGN
5.1 System Architecture
The architecture involves three parties: the cloud server, the third party auditor (TPA)
and users. There are two types of users in a group: the original user and a number of group
users.
The original user and group users are both members of the group. Group members are
allowed to access and modify shared data created by the original user based on access control
polices. Shared data and its verification information (i.e. signatures) are both stored in the
cloud server. The third party auditor is able to verify the integrity of shared data in the cloud
server on behalf of group members. Our system model includes the cloud server, the third
party auditor and users. The user is responsible for deciding who is able to share her data
before outsourcing data to the cloud. When a user wishes to check the integrity of shared
data, she first sends an auditing request to the TPA. After receiving the auditing request, the
TPA generates an auditing message to the cloud server, and retrieves an auditing proof of
shared data from the cloud server. Then the TPA verifies the correctness of the auditing
proof. Finally, the TPA sends an auditing report to the user based on the result of the
verification.
Fig 5.1: System Architecture

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5.2 Data Flow Diagram
A Data Flow diagram (DFD) is a graphical representation of the ―flow‖ of data
through an information system, modelling its process aspects. Often they are a preliminary
step used to create an overview of the system which can later be elaborated. DFD’s can also
be used for the visualization of data processing (structured design).
5.2.1 Level 0 Data Flow Diagram
Fig 5.2.1: Level 0 Data Flow Diagram
The step by step taken in the phase of input to output processing of data is shown in
this diagram. This includes user input and corresponding output the user receives.
5.2.2 Level 1 Data Flow Diagram-Admin
Fig 5.2.2: Level 1 Data Flow Diagram (Admin)
The Admin logins with valid username and password to the application. The admin has
access to the files verified and other details pertaining to the file. The admin overall has all

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the options like viewing of the files verified by the TPA and the changes made to the existing
files.
5.2.3 Level 2 Data Flow Diagram-Owner
Fig 5.2.3: Level 2 Data Flow Diagram
A owner is a person who can access resources from the cloud. The owner would first
register to the interface to get the services with the valid username and password. In order to
correctly audit the integrity of the entire data, a public verifier needs to choose the
appropriate public key for each block. Then they can request for the file to the cloud service
admin. There will be a third party auditor who performs the integrity checking of the data
before providing it to the owner or the users. This is done by 1st splitting the data into blocks
and then performing integrity check. The owner has the option of downloading the verified
file and also uploads new files.
5.2.4 Level 3 Data Flow Diagram-TPA
The TPA registers to the application with a valid username and password. TPA logins
to the application and verifies the integrity of data. TPA views all the list of files uploaded by
the owner without the key. Has the privilege of encrypting the data and save it on cloud.TPA
also view data which is uploaded by various owner.

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Fig 5.2.4: Level 3 Data Flow Diagram
5.3 Use Case Diagram of the system
A use case captures the interactions that occur between developers and users of
information and the system itself. The Use Case Diagram shows a number of external actors
and their connections to the use cases that the system provides. A use case is a description of
a functionality that the system provides.
The use case diagram presents an outside view of the system. The use-case model
consists of use-case diagrams consists of the following:
1. The use-case diagrams illustrate the actors, the use cases and their relationships.
2. Use cases also require a textual description (use case specification), as the visual
diagrams can’t contain all of the information that is necessary.
3. The customers, the end-users, the domain experts, and the developers all have an input
into the development of the use-case model.
The step by step taken in the phase of input to output processing of data is shown in this
diagram. This includes user input and corresponding output the user receives. The Admin
logins with valid username and password to the application. The admin has access to the files
verified and other details pertaining to the file.

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Fig 5.3 Use Case Diagram
A owner is a person who can access resources from the cloud. The owner would first
register to the interface to get the services with the valid username and password. In order to
correctly audit the integrity of the entire data, a public verifier needs to choose the
appropriate public key for each block. Then they can request for the file to the cloud service
admin. There will be a third party auditor who performs the integrity checking of the data
before providing it to the owner or the users. This is done by 1st splitting the data into blocks
and then performing integrity check. The owner has the option of downloading the verified
file and also uploads new files. The TPA registers to the application with a valid username
and password. TPA logins to the application and verifies the integrity of data. TPA views all
the list of files uploaded by the owner without the key. Has the privilege of encrypting the
data and save it on cloud.TPA also view data which is uploaded by various owner.
5.4 Sequence Diagram
A sequence diagram models a dynamic view of the interaction between model
elements at run time. Sequence diagrams are commonly used as explanatory models for use
case scenario. The figure 4.2 depicts the sequence diagram of the proposed system which is a

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structured representation of behavior as a series of sequential steps over time to achieve a
result.
Fig 5.4 Sequence Diagram

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CHAPTER 6
SYSTEM IMPLEMENTATION
6.1 Privacy Preserving Public Auditing module:
The details of our public auditing mechanism in Oruta includes: Key-Gen,
Sig-Gen, Modify, Proof-Gen and Proof Verify. In Key-Gen, users generate their own
public/private key pairs. In Sig-Gen, a user is able to compute ring signatures on blocks in
shared data. Each user is able to perform an insert, delete or update operation on a block, and
compute the new ring signature on this new block in Modify. Proof Gen is operated by the
TPA and the cloud server together to generate a proof of possession of shared data. In Proof-
Verify, the TPA verifies the proof and sends an auditing report to the user.
The proposed scheme is as follows:
 Setup Phase
 Audit Phase
Setup Phase
The user initializes the public and secret parameters of the system by executing
KeyGen, and pre-processes the data file F by using SigGen to generate the verification
metadata. The user then stores the data file F and the verification metadata at the cloud
server. The user may alter the data file F by performing updates on the stored data in cloud.
Audit Phase
TPA issues an audit message to the cloud server to make sure that the cloud server has
retained the data file F properly at the time of the audit. The cloud server will create a
response message by executing Genproof using F and its verification metadata as inputs. The
TPA then verifies the response by cloud server via Verify Proof.
A owner is a person who can access resources from the cloud. The owner would first
register to the interface to get the services with the valid username and password. In order to
correctly audit the integrity of the entire data, a public verifier needs to choose the
appropriate public key for each block. Then they can request for the file to the cloud service
admin. There will be a third party auditor who performs the integrity checking of the data
before providing it to the owner or the users. This is done by 1st splitting the data into blocks

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and then performing integrity check. The owner has the option of downloading the verified
file and also uploads new files.
6.2 Batch Auditing Module:
With the establishment of privacy-preserving public auditing in Cloud Computing,
TPA may concurrently handle multiple auditing delegations upon different users’ requests.
The individual auditing of these tasks for TPA can be tedious and very inefficient. Batch
auditing not only allows TPA to perform the multiple auditing tasks simultaneously, but also
greatly reduces the computation cost on the TPA side. Given K auditing delegations on K
distinct data files from K different users, it is more advantageous for TPA to batch these
multiple tasks together and audit at one time.
The TPA registers to the application with a valid username and password. TPA logins
to the application and verifies the integrity of data. TPA views all the list of files uploaded by
the owner without the key. Has the privilege of encrypting the data and save it on cloud.TPA
also view data which is uploaded by various owner.
6.3 Data Dynamics Module:
Supporting data dynamics for privacy-preserving public risk auditing is also of
paramount importance. Now we show how our main scheme can be adapted to build upon the
existing work to support data dynamics, including block level operations of modification,
deletion and insertion. We can adopt this technique in our design to achieve privacy-
preserving public risk auditing with support of data dynamics.
To enable each user in the group to easily modify data in the cloud and shared the
latest version of the data with rest of the group, oruta should also support dynamic operations
on shared data. An dynamic operation includes an insert, delete or update operation on a
single block. However, since the computation of a ring signature includes an identifier of a
block, traditional mrthods, which only use the index of a block as it’s identifier, are not
suitable for supporting dynamic operations on shared data. The reason is that, when user
modifies a single block in shared data by performing an insert or delete operation, the indices
of blocks that after the modified block are all changed and the changes of these indices
requires users to recompute the signatures of these blocks, even though the content of these
blocks are not modified.

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The details of our public auditing mechanism in Oruta includes: Key-Gen, Sig-Gen,
Modify, Proof-Gen and Proof Verify. In Key-Gen, users generate their own public/private
key pairs. In Sig-Gen, a user is able to compute ring signatures on blocks in shared data. Each
user is able to perform an insert, delete or update operation on a block, and compute the new
ring signature on this new block in Modify. Proof Gen is operated by the TPA and the cloud
server together to generate a proof of possession of shared data. In Proof-Verify, the TPA
verifies the proof and sends an auditing report to the user.
Modify: A user in the group modifies the block in the shared data by performing one
of the following three operations:
 Insert: The user inserts the new block say mj into shared data. Total number of
blocks in shared data is n. He/She computes the new identifier of the inserted
block mj as idj={vj,rj}where idj=identifier of jth block, vj=Virtual index. For the
rest of blocks, identifiers of these blocks are not changed. This user outputs
the new ring signature of the inserted block with SignGen, and uploads to the
cloud server. Total number of blocks in shared data increases to n+1.
 Delete: The user deletes the block mj , its identifier idj and ring signature from
the cloud server. The identifiers of other blocks in shared data are remains the
same. The total number of blocks in shared data in cloud decreases to n-1.
 Update: The user updates the jth
block in shared data with a new block mj. The
virtual index of this block remain the same and new ring signature is
computed. The user computes the new identifier of the updated block. The
identifiers of other blocks in shred data are not changed. The user outputs the
new ring signature of new block with SignGen, and uploads to the cloud
server. The total number of blocks in shared data is still n.
6.4 Algorithm:
The Advanced Encryption Standard (AES), also referenced as Rijndael (its original name), is
a specification for the encryption of electronic data established by the U.S. National Institute
of Standards and Technology (NIST) in 2001.The key size used for an AES cipher specifies
the number of repetitions of transformation rounds that convert the input, called the plaintext,
into the final output, called the cipher text. The number of cycles of repetition is as follows:
 10 cycles of repetition for 128-bit keys.
 12 cycles of repetition for 192-bit keys.

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 14 cycles of repetition for 256-bit keys.
Each round consists of several processing steps, each containing four similar but different
stages, including one that depends on the encryption key itself. A set of reverse rounds are
applied to transform cipher text back into the original plaintext using the same encryption
key.
High-level description of the algorithm
1. KeyExpansions—round keys are derived from the cipher key using Rijndael's key
schedule. AES requires a separate 128-bit round key block for each round plus one
more.
2. Initial Round
1. AddRoundKey—each byte of the state is combined with a block of the round
key using bitwise xor.
3. Rounds
1. Sub Bytes—a non-linear substitution step where each byte is replaced with
another according to a lookup table.
2. Shift Rows—a transposition step where the last three rows of the state are
shifted cyclically a certain number of steps.
3. Mix Columns—a mixing operation which operates on the columns of the state,
combining the four bytes in each column.
4. AddRoundKey
4. Final Round (no Mix Columns)
1. Sub Bytes
2. Shift Rows
3. AddRoundKey

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CHAPTER 7
SYSTEM TESTING
System testing of software or hardware is testing conducted on a complete, integrated
system to evaluate the system's compliance with its specified requirements. System testing
falls within the scope of black box testing, and as such, should require no knowledge of the
inner design of the code or logic.
The testing is important and is the final phase. All the process that has been done is
just a trail or by assumption. All the required hardware and software is prepared for the
testing so that errors or some modifications may be required for further proceeding.
The software, which has been developed, has to be tested to prove its validity. Testing
is considered to be the least creative phase of the whole cycle of system design, yet it is one
of the most important from the efficiency point of view. In the real sense it the phase, which
helps to bring out the other phases.
Testing is done to make sure that the product does exactly what is supposed to do.
Testing is the final verification and validation activity within the organization itself. In the
testing stage, try to achieve the following goals; to affirm the quality of the product, to find
and eliminate any residual errors from previous stages, to validate the software as a solution
to the original problem, to demonstrate the presence of all specified functionality in the
product, to estimate the operational reliability of the system. During testing the major
activities are concentrated on the examination and modification of the source code.
7.1 SOFTWARE TESTING STRATEGIES
Testing is vital to the success of the system. System testing makes a logical
assumption that if all parts of the system are correct. The preparation of testing should start as
soon as the design of the system starts. To carry out the testing in an efficient manner certain
amount of strategic planning has to be done. Any testing strategy must incorporate testing
planning, test case design, test execution and the resultant collection and evaluation. The goal
will be successfully achieved. There are four steps:
 Unit Testing
 Integration Testing
 Validation Testing
 Output Testing

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7.1.1 Unit Testing
In this testing, the smaller part of the project is tested first that is modules and the sub
functions present in the project. It seems to be working satisfactorily without any errors and
that shows the unit testing is successful.
7.1.2 Integration Testing
The integration testing is a part that the software makes all function behaviours and
process required. The errors which are uncovered are corrected during this phase. The
collections of the functions are tested and the one found with the errors are rectified so that
the result can be easily obtained in a successful manner.
7.1.3 Validation Testing
The validation part is very much essential for every project so that each data can be
validated in a good manner. In some case the records are created according to the key of the
corresponding table to which it has been referenced for data constraint for good secured
database. While testing the system again the errors which are not corrected are corrected by
using the above testing steps and the corrections are noted. If there is any error then it is
allowed for testing from the beginning.
7.1.4 Output Testing
The output testing is the major part of the project. The output is tested for required
format. If it does not acquire required format then screen modification is done. The output
testing is mainly for two things they are on screen format and print format. The screen is
found correct done by user which is required for the hard copy and the output arrives as
specified by the user. Hence the output testing doesn’t result in any correction in the system
7.2 Test Cases
Unit testing is usually conducted as part of a combined code and unit test phase of the
software lifecycle, although it is not uncommon for coding and unit testing to be conducted as
two distinct phases.
Test strategy and approach
Field testing will be performed manually and functional tests will be written in detail.
Test objectives
 All field entries must work properly. 

 Pages must be activated from the identified link. 

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
 The entry screen, messages and responses must not be delayed. 
Features to be tested
 Verify that the entries are of the correct format 

 No duplicate entries should be allowed 

 All links should take the user to the correct page.
Sl. No of Test Case 02
Name of the Test To verify the functionality of the Browse Button
Feature being Tested File format
Description Click on the Browse button to get the file dialog window,
browse through directories and file to be compressed.
Sample Input File of multiple format
Expected Output Multiple files have to be uploaded successfully
Actual Output If trying to upload multiple files then throws error message to
select one file at a time
Sl. No of Test Case 01
Name of the Test To verify the functionality of the Browse button
Feature being Tested File format
Description
Click on the Browse button to get the file dialog window,
browse through directories and file to be compressed.
Sample Input File of desired format
Expected Output The size of the uploaded file is shown below.
Actual output As Expected
Remarks Test case passed.

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Remarks Test case failed
Sl. No of Test Case 03
Name of the Test To verify the functionality of the Clear Button
Feature being Tested Is data cleared or not?
Description New data can be entered by clearing existing
Sample Input Data
Expected Output Clears entered data
Actual Output As expected
Remarks Test case Passed
Sl. No of Test Case 04
Name of the Test To verify the functionality of the Get Key
Feature being Tested Checking security key is sent to mail or not
Description Checking mail to retrieve security key
Sample Input E-mail id
Expected Output Get Key link appears if its offline
Actual output On clicking get key button key is retrieved
Remarks Test case passed

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Integration Testing:
Software integration testing is the incremental integration testing of two or more
integrated software components on a single platform to produce failures caused by interface
defects. The task of the integration test is to check that components or software applications,
e.g. components in a software system or – one step up – software applications at the
company level – interact without error.
Test Results: All the test cases mentioned above passed successfully. No defects
encountered.
Acceptance Testing:
User Acceptance Testing is a critical phase of any project and requires significant
participation by the end user. It also ensures that the system meets the functional
requirements.
Test Results: All the test cases mentioned above passed successfully. No defects
encountered.

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CHAPTER 8
RESULTS
We now evaluate the efficiency of Oruta in experiments. Below is the table of
comparision with existing mechanisms.
PDP: ―Provable data
possession at
untrusted stores‖
WWRL:‖Privacy-
Preserving Public
Auditing for Data
Storage Security in
Cloud Computing‖
Oruta
Public Auditing Yes Yes Yes
Data Privacy No Yes Yes
Identity Privacy No No Yes
According to Privacy Preserving Public Auding for shared Data in the cloud the generation
time of the ring signature on a block is determined by the number of users in the group and
number of elements in each block.

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CHAPTER 9
CONCLUSION
We propose Oruta, the first privacy preserving public auditing mechanism for shared
data in the cloud. We utilize ring signatures to construct homomorphic authenticators, so the
TPA is able to audit the integrity of shared data, yet cannot distinguish who is the signer on
each block, which can achieve identity privacy. To improve the efficiency of verification for
multiple auditing tasks, we further extend our mechanism to support batch auditing. An
interesting problem in our future work is how to efficiently audit the integrity of shared data
with dynamic groups while still preserving the identity of the signer on each block from the
third party auditor.

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REFERENCES
[1] M. Armbrust, A. Fox, R. Griffith, A. D.Joseph, R. H.Katz, A. Konwinski, G. Lee, D. A.
Patterson, A. Rabkin, I. Stoica, and M. Zaharia, ―A View of Cloud Computing,‖
Communications of the ACM, vol. 53, no. 4, pp. 50–58, Apirl 2010.
[2] G. Ateniese, R. Burns, R. Curtmola, J. Herring, L. Kissner, Z. Peterson, and D. Song,
―Provable Data Possession at Untrusted Stores,‖ in Proc. ACM Conference on Computer and
Communications Security (CCS), 2007, pp. 598–610.
[3] C. Wang, Q. Wang, K. Ren, and W. Lou, ―Privacy-Preserving Public Auditing for Data
Storage Security in Cloud Computing,‖ in Proc. IEEE International Conference on Computer
Communications (INFOCOM), 2010, pp. 525–533.
[4] R. L. Rivest, A. Shamir, and Y. Tauman, ―How to Leak a Secret,‖ in Proc. International
Conference on the Theory and Application of Cryptology and Information Security
(ASIACRYPT). SpringerVerlag, 2001, pp. 552–565.
[5] D. Boneh, C. Gentry, B. Lynn, and H. Shacham, ―Aggregate and Verifiably Encrypted
Signatures from Bilinear Maps,‖ in Proc. International Conference on the Theory and
Applications of Cryptographic Techniques (EUROCRYPT). Springer-Verlag, 2003, pp. 416–
432.
[6] H. Shacham and B. Waters, ―Compact Proofs of Retrievability,‖ in Proc. International
Conference on the Theory and Application of Cryptology and Information Security
(ASIACRYPT). SpringerVerlag, 2008, pp. 90–107.
[7] Y. Zhu, H. Wang, Z. Hu, G.-J. Ahn, H. Hu, and S. S.Yau, ―Dynamic Audit Services for
Integrity Verification of Outsourced Storage in Clouds,‖ in Proc. ACM Symposium on
Applied Computing (SAC), 2011, pp. 1550–1557.
[8] S. Yu, C. Wang, K. Ren, and W. Lou, ―Achieving Secure, Scalable, and Fine-grained
Data Access Control in Cloud Computing,‖ in Proc. IEEE International Conference on
Computer Communications (INFOCOM), 2010, pp. 534–542.
[9] D. Boneh, B. Lynn, and H. Shacham, ―Short Signature from the Weil Pairing,‖ in Proc.
International Conference on the Theory and Application of Cryptology and Information
Security (ASIACRYPT). Springer-Verlag, 2001, pp. 514–532.

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APPENDIX –A SCREEN SHOTS
ORUTA- Privacy Preserving Public Auditing for Shared Data in
the Cloud – Main Page

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Security key is sent to Email when Owner tries to Login
Security Key is retrieved from mail for owner login

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Owner can upload files
Encrypted and Decrypted key is generated for file
download

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Download Verification- Requesting for Encrypted &
Decrypted key
Cryptographic key request is sent to owner for Download
verification

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Cryptographic Key is sent to TPA- TPA can download the
uploaded file
If any modifications are required then TPA modifies the
downloaded file and uploads

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Admin informs the file Owner about the modified file

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These are the Owner files which have no modifications

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APPENDIX-B SAMPLE CODE
File verification: Direct or Download Verification:
using System;
using System.Collections;
using System.Configuration;
using System.Data;
using System.Linq;
using System.Web;
using System.Web.Security;
using System.Web.UI;
using System.Web.UI.HtmlControls;
using System.Web.UI.WebControls;
using System.Web.UI.WebControls.WebParts;
using System.Xml.Linq;
using System.Data.SqlClient;
public partial class tpaverify1 : System.Web.UI.Page
{
SqlConnection con = new
SqlConnection(ConfigurationManager.AppSettings["ConnectionString"]);
string fileid, keyrequest = "YES";
protected void Page_Load(object sender, EventArgs e)
{
fileid = (string)Request.Params["ID"];
Session["FileID"] = fileid;
SqlDataAdapter adp = new SqlDataAdapter("Select * from filearchive where fid='" +
fileid + "'", con);
DataSet ds = new DataSet();
adp.Fill(ds);
Label4.Text = ds.Tables[0].Rows[0]["fid"].ToString();
Label7.Text = ds.Tables[0].Rows[0]["ffilename"].ToString();

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Label10.Text = ds.Tables[0].Rows[0]["fsubject"].ToString();
Label13.Text = ds.Tables[0].Rows[0]["fext"].ToString();
Label16.Text = ds.Tables[0].Rows[0]["fsizeinkb"].ToString();
Label19.Text = ds.Tables[0].Rows[0]["fdatetime"].ToString();
Label23.Text = ds.Tables[0].Rows[0]["fverify"].ToString();
Label29.Text = ds.Tables[0].Rows[0]["keyrequest"].ToString();
if (Label23.Text == "YES" || Label29.Text == "YES")
{
Button1.Enabled = false;
Button2.Enabled = false;
if (Label23.Text == "YES")
{
string myStringVariable1 = string.Empty;
myStringVariable1 = "File already verified.";
ClientScript.RegisterStartupScript(this.GetType(), "myalert", "alert('" +
myStringVariable1 + "');", true);
}
else
{
if (Label23.Text == "NO" && Label29.Text == "YES")
{
string myStringVariable1 = string.Empty;
myStringVariable1 = "This file request for key proceesing.";
ClientScript.RegisterStartupScript(this.GetType(), "myalert", "alert('" +
myStringVariable1 + "');", true);
}
}
}
}
protected void LinkButton1_Click(object sender, EventArgs e)
{
Response.Redirect("tpaverify.aspx");
}

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protected void btnyes_Click(object sender, EventArgs e)
{
con.Open();
SqlCommand cmd1 = new SqlCommand("update filearchive set keyrequest='" +
keyrequest + "' where fid='" + Label4.Text + "'", con);
cmd1.ExecuteNonQuery();
con.Close();
Label25.Text = "Cryptography Key Request Sent To Owner.";
Label25.ForeColor = System.Drawing.ColorTranslator.FromHtml("#088A08");
btnyes.Visible = false;
btnno.Visible = false;
Button1.Enabled = false;
Label24.Visible = true;
Button2.Enabled = false;
ModalPopupExtender1.Show();
Panel3.Visible = true;
}
protected void btnno_Click(object sender, EventArgs e)
{
Page_Load(null, EventArgs.Empty);
}
protected void ImageButton2_Click(object sender, ImageClickEventArgs e)
{
Page_Load(null, EventArgs.Empty);
}
protected void Button1_Click(object sender, EventArgs e)
{
Response.Redirect("tpadirectvermod.aspx");
}}

45. ORUTA: Privacy Preserving Public Auditing for Shared Data in the Cloud
Dept. Of ISE, AMCEC 2014-2015 Page 45
List of Files Verified and need to be Verified:
using System;
using System.Collections;
using System.Configuration;
using System.Data;
using System.Linq;
using System.Web;
using System.Web.Security;
using System.Web.UI;
using System.Web.UI.HtmlControls;
using System.Web.UI.WebControls;
using System.Web.UI.WebControls.WebParts;
using System.Xml.Linq;
using System.Data.SqlClient;
public partial class tpaverify : System.Web.UI.Page
{
SqlConnection con = new
SqlConnection(ConfigurationManager.AppSettings["ConnectionString"]);
protected void Page_Load(object sender, EventArgs e)
{
SqlDataAdapter adp1 = new SqlDataAdapter("Select * from filearchive", con);
DataSet ds1 = new DataSet();
adp1.Fill(ds1);
GridView1.DataSource = ds1;
GridView1.DataBind();
}
protected void GridView1_PageIndexChanging(object sender, GridViewPageEventArgs e)
{
GridView1.PageIndex = e.NewPageIndex;

46. ORUTA: Privacy Preserving Public Auditing for Shared Data in the Cloud
Dept. Of ISE, AMCEC 2014-2015 Page 46
bindgrid();
}
public void bindgrid()
{
SqlDataAdapter adp1 = new SqlDataAdapter("Select * from filearchive", con);
DataSet ds1 = new DataSet();
adp1.Fill(ds1);
GridView1.DataSource = ds1;
GridView1.DataBind();
}
}
Admin View the TPA Modified files:
using System;
using System.Collections;
using System.Configuration;
using System.Data;
using System.Linq;
using System.Web;
using System.Web.Security;
using System.Web.UI;
using System.Web.UI.HtmlControls;
using System.Web.UI.WebControls;
using System.Web.UI.WebControls.WebParts;
using System.Xml.Linq;
using System.Data.SqlClient;
public partial class AdminVerify : System.Web.UI.Page
{
SqlConnection con = new
SqlConnection(ConfigurationManager.AppSettings["ConnectionString"]);
string fmodify = "NO", fverify = "YES";
protected void Page_Load(object sender, EventArgs e)
{

47. ORUTA: Privacy Preserving Public Auditing for Shared Data in the Cloud
Dept. Of ISE, AMCEC 2014-2015 Page 47
SqlDataAdapter adp = new SqlDataAdapter("Select * from filearchive where fmodify='"
+ fmodify + "' and fverify='" + fverify + "'", con);
DataSet ds = new DataSet();
adp.Fill(ds);
GridView1.DataSource = ds;
GridView1.DataBind();
Label20.Text = Convert.ToString("(" + ds.Tables[0].Rows.Count + ")");
}
protected void GridView1_PageIndexChanging(object sender, GridViewPageEventArgs e)
{
GridView1.PageIndex = e.NewPageIndex;
bindgrid();
}
public void bindgrid()
{
SqlDataAdapter adp = new SqlDataAdapter("Select * from filearchive where fmodify='"
+ fmodify + "' and fverify='" + fverify + "'", con);
DataSet ds = new DataSet();
adp.Fill(ds);
GridView1.DataSource = ds;
GridView1.DataBind();
}
protected void LinkButton1_Click(object sender, EventArgs e)
{
Response.Redirect("AdminMain.aspx");
}
}