TOWARDS SECURE & DEPENDABLE
STORAGE SERVICES IN CLOUD COMPUTING

Guided By
Prof. Dinesh Singh
Dept of CSE, PESIT

Presente...
SYNOPSIS
•
•
•
•
•
•

INTRODUCTION
EXISTING SYSTEM
PROPOSED SYSTEM
ALGORITHMS
APPLICATIONS
CONCLUSION
INTRODUCTION
 Enjoy the cloud applications without the burden of local
hardware and software management.
 The proposed d...
EXISTING SYSTEM AND LIMITATIONS
 Byzantine Error Occurrence.
 Does not guarantee the data availability in case of
server...
PROPOSED SYSTEM
 Flexibale Distributed Adutiting Mechanism
 Storage Correctness
 Dynamic Data Support
 Delete , append...
PROPOSED SYSTEM ARCHITECTURE
DESCRIPTION
There are Two models is the proposed system
1. SYSTEM MODEL
 User:
 Cloud Server(CS)
 Third Party Auditor (...
ALGORITHM’S USED

 Token Pre-computation
 Correctness Verification and Error Localization

 Error recovery
TOKEN PRE-COMPUTATION
 Before file distribution the user pre-computes a certain number of short
verification tokens on in...
CORRECTNESS VERIFICATION AND ERROR
LOCALIZATION
 Error localization is a key prerequisite for eliminating errors in stora...
ERROR RECOVERY
 The user can reconstruct the original file by downloading the data
vectors from the first m servers, assu...
APPLICATION
 MICROSOFT APP’s
 GOOGLE Docs
 OFFICE 365
 FINANCE
 AMAZON web services
CONCLUSION
 In this scheme is to achieve the assurance of cloud data integrity
and availability
 Fast localization
 Dyn...
When Social Web Meets Semantic Web : Web 2.5

REFERENCES
[1] C. Wang, Q. Wang, K. Ren, and W. Lou, “Ensuring data storage ...
Toward secure and dependable storage services in cloud computing 2
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Toward secure and dependable storage services in cloud computing 2

  1. 1. TOWARDS SECURE & DEPENDABLE STORAGE SERVICES IN CLOUD COMPUTING Guided By Prof. Dinesh Singh Dept of CSE, PESIT Presented By Mahesh Gonda MTech CSE , 1st Sem , Roll No - 29
  2. 2. SYNOPSIS • • • • • • INTRODUCTION EXISTING SYSTEM PROPOSED SYSTEM ALGORITHMS APPLICATIONS CONCLUSION
  3. 3. INTRODUCTION  Enjoy the cloud applications without the burden of local hardware and software management.  The proposed design allows users to audit the cloud storage with very lightweight communication and computation cost.  The proposed design further supports secure and efficient dynamic operations on outsourced data, including block modification , deletion, and append.  The proposed scheme is highly efficient and resilient against Byzantine failure, malicious data modification attack, and even server colluding attacks
  4. 4. EXISTING SYSTEM AND LIMITATIONS  Byzantine Error Occurrence.  Does not guarantee the data availability in case of server failures.  In this system doesn’t have any correctness measure of data
  5. 5. PROPOSED SYSTEM  Flexibale Distributed Adutiting Mechanism  Storage Correctness  Dynamic Data Support  Delete , append and modification of blocks  Utilizing the Homomorphic Token with distributed verification of erasure-coded data for identification of the misbehaving server(s)  Highly efficient and resilent against  Byzantine failures  Malicious data modification attack  Server colluding attacks.
  6. 6. PROPOSED SYSTEM ARCHITECTURE
  7. 7. DESCRIPTION There are Two models is the proposed system 1. SYSTEM MODEL  User:  Cloud Server(CS)  Third Party Auditor (TPA) . 2. ADVERSARY MODEL
  8. 8. ALGORITHM’S USED  Token Pre-computation  Correctness Verification and Error Localization  Error recovery
  9. 9. TOKEN PRE-COMPUTATION  Before file distribution the user pre-computes a certain number of short verification tokens on individual vector.  User wants to make sure the storage correctness for the data in the cloud, he challenges the cloud servers with a set of randomly generated block indices.  Each cloud server computes a short “signature” over the specified blocks and returns them to the user.
  10. 10. CORRECTNESS VERIFICATION AND ERROR LOCALIZATION  Error localization is a key prerequisite for eliminating errors in storage systems.  Proposed scheme outperforms correctness verification and error localization (misbehaving server identification)  The response values from servers for each challenge not only determine the correctness of the distributed storage, but also contain information to locate potential data error(s).
  11. 11. ERROR RECOVERY  The user can reconstruct the original file by downloading the data vectors from the first m servers, assuming that they return the correct response values.  The data corruption is detected, the comparison of pre-computed tokens and received response values can guarantee the identification of misbehaving server(s) (again with high probability)
  12. 12. APPLICATION  MICROSOFT APP’s  GOOGLE Docs  OFFICE 365  FINANCE  AMAZON web services
  13. 13. CONCLUSION  In this scheme is to achieve the assurance of cloud data integrity and availability  Fast localization  Dynamic Data Support  Dependability
  14. 14. When Social Web Meets Semantic Web : Web 2.5 REFERENCES [1] C. Wang, Q. Wang, K. Ren, and W. Lou, “Ensuring data storage security in cloud computing,” in Proc. of IWQoS’09, July 2009, pp. 1–9. [2] Amazon.com, “Amazon web services (aws),” Online at http:// aws.amazon.com/, 2009. [3] Sun Microsystems, Inc., “Building customer trust in cloud computing with transparent security,” Online at https://www.sun.com/offers/details/sun transparency.xml , November 2009. [4] M. Arrington, “Gmail disaster: Reports of mass email deletions,” Online at http://www.techcrunch.com/2006/12/28/gmail-disasterreports-of-mass-email-deletions/ , December 2006. [5] J. Kincaid, “MediaMax/TheLinkup Closes Its Doors,” Online at http://www.techcrunch.com/2008/07/10/mediamaxthelinkup-closes-its-doors/ , July 2008. 14

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