This document proposes a privacy-preserving public auditing system for cloud data storage. It allows an external third party auditor (TPA) to audit user's outsourced data stored in the cloud without learning the data content. The proposed scheme supports batch auditing where the TPA can perform multiple auditing tasks simultaneously. It utilizes public key based homomorphic authenticators and random masking techniques to achieve privacy-preserving public auditing for cloud data storage.

1. PRIVACY-PRESERVING
PUBLIC AUDITING FOR DATA
SECURITY IN CLOUD
COMPUTING
By,
STORAGE
S.Kayalvizhi Sharmila

2. ABSTRACT
 Cloud Computing, enabling convenient network
access to a shared pool of configurable computing
resources
 Enabling public auditability, so that users can resort
to an external audit party to check the integrity of
outsourced data when needed.
 TPA audit the cloud data storage without
demanding the local copy of data, and introduce no
additional on-line burden to the cloud user.

3. OUR CONTRIBUTION
The scheme supports an external auditor to audit
user’s outsourced data in the cloud without learning
knowledge on the data content.
Achieves batch auditing where multiple delegated
auditing tasks from different users can be
performed simultaneously by the TPA.
To prove the security and justify the performance of
proposed schemes through concrete experiments
and comparisons with the state-of-the-art.

4. THIRD PARTY AUDITOR (TPA)
 Third Party Auditor (TPA)
 TPA helps the user to audit the data
 To allow TPA securely:
 1) TPA should audit the data from the cloud, not ask for a copy
 2) TPA should not create new vulnerability to user data privacy
 This paper presents a privacy-preserving public auditing
system for cloud data storage
Data
Cloud network
user
user
user
External
Audit party

5. ARCHITECTURE

6. DESIGN GOALS
I. Public auditability
II. Storage correctness
III. Privacy-preserving
IV. Batch auditing
V. Lightweight

7. THE EXISTING SYSTEM

8. THE PROPOSED SCHEME
 We utilize the public key based homomorphic
authenticator and uniquely integrate it with random
mask technique.
 TPA can perform multiple auditing tasks
simultaneously.
 Four algorithms
KeyGen, SigGen, GenProof, VerifyProof.

9. FIG: PROPOSED SYSTEM

10. PRIVACY-PRESERVING PUBLIC AUDITING MODULE:
 Homomorphic authenticators are unforgeable
verification metadata.
Homomorphic authenticator
Block 1 Block 2 … Block k
Verification
Metadata
Verification
Metadata
Verification
Metadata
Aggregate Verification
Metadata
A linear combination of data blocks can be verified by
looking only at the aggregated authenticator

11. 11
Setup
Audit
user KeyGen
Public & Secret
parameters
SigGen File F
Verification
Metadata
TPA
TPA issues an audit message or a challenge to CSP
GenProof
VerifyProof
CSP
TPA
File F
Response message
Verification Metadata
Phases

12. MORE EXTENSIONS
 Batch auditing
 There are K users having K files on the same cloud
 They have the same TPA
 Then, the TPA can combine their queries and save in
computation time
 The comparison function that compares the aggregate
authenticators has a property that allows checking multiple
messages in one equation
 Instead of 2K operation, K+1 are possible
 Data dynamics
 The data on the cloud may change according to applications
 This is achieved by using the data structure Merkle Hash Tree
(MHT)
 With MHT, data changes in a certain way; new data is added in
some places
 There is more overhead involved ; user sends the tree root to
TPA

13. CONCLUSION
 Utilizing the homomorphic authenticator and
random mask technique to guarantee that TPA
would not learn any knowledge about the data
content
 Considering TPA may concurrently handle multiple
audit sessions from different users for their
outsourced data files