Privacy Preserving Remote Data Integrity Checking Protocol With Data Dynamics and Public Verifiability

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Abstract:

                Remote data integrity checking is a crucial technology in cloud computing. Recently many works focus on providing data dynamics and/or public verifiability to this type of protocols. Existing protocols can support both features with the help of a third party auditor. In a previous work, propose a remote data integrity checking protocol that supports data dynamics. In this paper, we adapt to support public verifiability. The proposed protocol supports public verifiability without help of a third party auditor. In addition, the proposed protocol does not leak any private information to third party verifiers. Through a formal analysis, we show the correctness and security of the protocol. After that, through theoretical analysis and experimental results, we demonstrate that the proposed protocol has a good performance.

Architecture:

A Privacy-Preserving Remote Data Integrity Checking Protocol with Data Dynamics and Public Verifiability

Existing System:

                          In existing system, the clients store the data in server that server is trustworthy and after the third party auditor can audit the client files. So, the third party auditor can stolen the files.

Disadvantage:

                     Existing protocols can support both features with the help of a third party auditor.

Proposed System:

                         We consider a cloud storage system in which there are a client and an untrusted server. The client stores their data in the server without keeping a local copy. Hence, it is of critical importance that the client should be able to verify the integrity of the data stored in the remote untrusted server. If the server modifies any part of the client’s data, the client should be able to detect it; furthermore, any third party verifier should also be able to detect it. In case a third party verifier verifies the integrity of the client’s data, the data should be kept private against the third party verifier.

 Advantages:

 In this paper, we have the following main contributions:

                           • We propose a remote data integrity checking protocol for cloud storage. The proposed protocol inherits the support of data dynamics, and supports public verifiability and privacy against third-party verifiers, while at the same time it doesn’t need to use a third-party auditor.

                        • We give a security analysis of the proposed protocol, which shows that it is secure against the untrusted server and private against third party verifiers.

Modules:

                     1. Data Dynamics

                                                            i.      Block Insertio

                                                           ii.      Block Modification

                                                          iii.      Block Deletion

                     2.  public verifiability

                     3.  Metadata Generation

                     4.  Privacy against Third Party Verifiers

 1.     Data Dynamics:

                               Data dynamics means after clients store their data at the remote server, they can dynamically update their data at later times. At the block level, the main operations are block insertion, block modification and block deletion.

  i.            Block Insertion:

                                          The Server can insert anything on the client’s file.

ii.            Block Deletion:

                                          The Server can delete anything on the client’s file.

iii.            Block Modification:

                                          The Server can modify anything on the client’s file.

 2.     public verifiability:

                                Each and every time the secret key sent to the client’s email and can perform the integrity checking operation.  In this definition, we have two entities: a challenger that stands for either the client or any third party verifier, and an adversary that stands for the untrusted server. Client doesn’t ask any secret key from third party.

 3.     Metadata key Generation:

                    Let the verifier V wishes to the store the file F. Let this file F consist of n file blocks. We initially preprocess the file and create metadata to be appended to the file. Let each of the n data blocks have m bits in them. A typical data file F which the client wishes to store in the cloud.

Each of the Meta data from the data blocks m_i is encrypted by using a suitable algorithm to give a new modified Meta data M_i. Without loss of generality we show this process. The encryption method can be improvised to provide still stronger protection for Client’s data. All the Meta data bit blocks that are generated using the procedure are to be concatenated together. This concatenated Meta data should be appended to the file F before storing it at the cloud server. The file F along with the appended Meta data with the cloud.

 4.     Privacy against Third Party Verifiers:

                              Under the semi-honest model, a third party verifier cannot get

Any information about the client’s data m from the protocol execution. Hence, the protocol is private against third party verifiers. If the server modifies any part of the client’s data, the client should be able to detect it; furthermore, any third Party verifier should also be able to detect it. In case a third party verifier verifies the integrity of the client’s data, the data should be kept private against the third party verifier.

 Algorithm:

RSA & Metadata Generation:

The  input, and outputs R = gs_n i=1 aimi mod N, in which ai = fr(i) for i ∈ [1, n]. Because A can naturally computes P = g_n i=1 aimi mod N from Dm, P is also treated as A’s output. So A is given (N, g, gs) as input, and outputs (R, P) that satisfies R = Ps. From the KEA1-r assumption, B can construct an extractor A￣, which given the same input as A, outputs c which satisfies P = gc mod N. As P =

g_n i=1 aimi mod N, B extracts c = _ni=1 aimi mod p_q_.Now B generates n challenges _r1, gs1_, _r2, gs2_, …,_rn, gsn_ using the method described in section III. Bcomputes aji = frj (i) for i ∈ [1, n] and j ∈ [1, n]. Because {r1, r2, …, rn} are chosen by B, now B chooses them so that {aj 1, aj 2, …, aj n }, j = 1, 2, …, n

System Specification:

Hardware Requirements:

• System                        :   Pentium IV 2.4 GHz.
• Hard Disk      :   40 GB.
• Floppy Drive   :   1.44 Mb.
• Monitor           :   15 VGA Colour.
• Mouse             :   Sony.
• Ram                 :   512 Mb.

Software Requirements:

• Operating system        :   Windows XP.
• Coding Language       :   ASP.Net with C#
• Data Base                    :   SQL Server 2005.