WELCOME

PRIVACY-PRESERVING

PUBLIC AUDITING FOR DATA STORAGE

SECURITY IN CLOUD

COMPUTING

PRESENTED BYRAFA MAHAMOOD S VSHAMEEMA K VSUMAYYA K P

Introduction System and threat model Proposed scheme Security analysis & performance

evaluation

OUTLINE

Cloud computing gives flexibility to users Users pay as much as they use Users don’t need to set up the large

computers But the operation is managed by the Cloud

Service Provider (CSP) The user give their data to CSP; CSP has

control on the data The user needs to make sure the data is

correct on the cloud Internal (some employee at CSP) and external

(hackers) threats for data integrity CSP might behave unfaithfully

For money reasons, CSP might delete data that’s rarely accessed

CSP might hide data loss to protect their reputation

INTRODUCTION

How to efficiently verify the correctness of outsourced data?Simply downloading the data by the user

is not practical TPA can do it and provide an audit

report TPA should not read the data content

Legal regulations: US Health Insurance Portability and Accountability Act (HIPAA)

This paper presents how to enable privacy-preserving third-party auditing protocolFirst work in the literature to do this

RESPONSIBLE USERS

SYSTEM AND THREAT MODEL U: cloud user has a large amount of data files to

store in the cloud CS: cloud server which is managed by the CSP and

has significant data storage and computing power (CS and CSP are the same in this paper)

TPA: third party auditor has expertise and capabilities that U and CSP don’t have. TPA is trusted to assess the CSP’s storage security upon request from U

SYSTEM ANALYSIS

EXISTING SYSTEM Controlled by the entity,& restricted by them to the

authorised users Delivered via the internet to all users Not secure

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Uses homomorphic authenticator Also uses a random mask achieved by a Pseudo Random

Function (PRF)

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

Homomorphic authenticator

PROPOSED SYSTEM

SYSTEM REQUIREMENTS

HARDWARE SPECIFICATION Processor : Pentium IV or

above Memory : 2GB or above Hard Disk : 120 GB or above

RECOMMENDED SOFTWARE

• Operating System : Windows 7• Programming environment : Java• IDE : Eclipse•Java Version : JDK 1.6 or later•Google Pluggin for Eclipse

WHAT IS CLOUD COMPUTING??? With cloud computing, users can remotely

store their data into the cloud and use on-demand high-quality applications

Using a shared pool of configurable computing resources

Data outsourcing: users are relieved from the burden of data storage and maintenance

When users put their data (of large size) on the cloud, the data integrity protection is challenging

Enabling public audit for cloud data storage security is important

Users can ask an external audit party to check the integrity of their outsourced data

Cloud network

datauser

user

user

External Audit party

External audit party is called 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 copy2) TPA should not create new vulnerability to

user data privacy We presents a privacy-preserving public

auditing system for cloud data storage

THIRD PARTY AUDITOR (TPA)

A NOTE ON AUDITING What’ is auditing?

A PUBLIC AUDITING SCHEME Consists of four algorithms (KeyGen, SigGen, GenProof, VerifyProof)

KeyGen: key generation algorithm that is run by the user to setup the scheme

SigGen: used by the user to generate verification metadata, which may consist of MAC, signatures or other information used for auditing

GenProof: run by the cloud server to generate a proof of data storage correctness

VerifyProof: run by the TPA to audit the proof from the cloud server

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Setup

Audit

user KeyGen

Public & Secretparameters

SigGen File F

Verification

MetadataTPA

TPA issues an audit message or a challenge to

CSP

GenProof

VerifyProof

CSP

TPA

File F

Response message

Verification Metadata

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BASIC SCHEME 1

MAC

key

File block

code

Message Authentication Code (MAC)

Block 1 Block nBlock 2 …

File is divided into blocks

Cloud

user

TPA

Block 1 Block n…Block 2

code 1 code n…code 2

-User computes the MAC of every file block-Transfers the file blocks & codes to cloud-Shares the key with TPA

Audit-TPA demands a random number of blocks and their code from CSP-TPA uses the key to verify the correctness of the file blocks

Drawbacks: -The audit demands retrieval of user’s data; this is not privacy-preserving-Communication and computation complexity are linear with the sample size

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BASIC SCHEME 2Block 1 Block n…Block 2

code 1 code n…code 2

code 1 code n…code 2

code 1 code n…code 2

Key 1

Key 2

Key s

…

user

CloudTPA

Block 1 Block m…Block 2

Setup-User uses s keys and computes the MAC for blocks-User shares the keys and MACs with TPA

Audit-TPA gives a key (one of the s keys) to CSP and requests MACs for the blocks-TPA compares with the MACs at the TPA-Improvement from Scheme 1: TPA doesn’t see the data, preserves privacy-Drawback: a key can be used once.-The TPA has to keep a state; remembering which key has been used-Schemes 1 & 2 are good for static data (data doesn’t change at the cloud)

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Uses homomorphic authenticator Also uses a random mask achieved by a Pseudo Random

Function (PRF)

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

Homomorphic authenticator

Proposed scheme

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Random Mask by PRF

PRIVACY-PRESERVING PUBLIC AUDITING SCHEME

-The PRF function masks the data-It has a property of not affecting the Verification Metadata

Block 1

Verification

Metadata

Block 1 withPRF Mask

Verification

Metadata

Block 1

Equal

- In addition to Aggregate Authenticator, the TPA will receive a linear combination of file blocks:

vi are random numbermi are file blocks

-If TPA sees many linear combinations of the same blocks, it might be able to infer the file blocks

-This, we also use a random mask provided by the Pseudo Random Function (PRF)

r is the mask

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user KeyGen

Public key (sk)&Secret key (pk)

Setup

SigGenusersk

Block 1 Block 2 Block n…

σ1 …σ2 σn

Block 1 Block n…Block 2

σ1 … σnσ2

1- User generates public and secret

parameters

2- A code is generated for each file block

3- The file blocks and their codes are transmitted to the

cloudAudit

-TPA sends a challenge message to CSP-It contains the position of the blocks that will be checked in this audit

GenProofCSP

Selected blocks in challenge

Aggregate authenticator

-CSP also makes a linear combination of selected blocks and applies a mask. Separate PRF key for each auditing.-CSP send aggregate authenticator & masked combination of blocks to TPA

VerifyProofTPA

Masked linear combination of requested blocks

Aggregate authenticator

Compare the obtained Aggregate authenticator to the one received from CSP

The data sent from CSP to TPA is independent of the data sizeLinear combination with mask

Previous work has shown that if the server is missing 1% of the dataWe need 300 or 460 blocks to detect that

with a probability larger than 95% or 99%, respectively

PROPERTIES

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

MORE POSSIBLE EXTENSIONS

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 This scheme is not evaluated in the paper

Reference [11] doesn’t have privacy-preserving propertyTPA can read the information

PERFORMANCE

BATCH AUDITING Number of auditing tasks increased from 1

to 200 in multiple of 8 Auditing time per task: total auditing time /

number of tasks

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PERFORMANCE WITH INVALID RESPONSES

In batch auditing, true means that all of the messages are correct

False means at least one is wrongDivide batch in half, repeat for left- and right partsBinary search

1 2 3 4 5 6 7 8 9 10Wrong

1 2 3 4 5 6 7 8 9 10Wrong

1 2 3 4 5 6 7 8 9 101,2,3 and 9,10

1 2 3 4 5 6 7 8 9 103 and 10

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The more errors that there is, it takes more time to find them

CONCLUSION Utilize the homomorphic linear authenticator and

random masking to guarantee that the TPA would not learn any knowledge about the data content stored on the cloud server during the efficient auditing process.

Eliminates the burden of cloud user from the tedious and possibly expensive auditing task and alleviates the users’

fear of their outsourced data leakage TPA may concurrently handle multiple audit sessions

from different users for their outsourced data files. Extend our privacy-preserving public auditing protocol

into a multi-user setting, where the TPA can perform multiple auditing tasks in a batch manner for better efficiency

Schemes are provably secure and highly efficient