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Enabling Efficient Batch Verification Enabling Efficient Batch Verification on Data Integrity for Cloud on Data Integrity for Cloud ChinLaung Lei Department of Electrical Engineering National Taiwan University 1 Outline Introduction

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Enabling Efficient Batch Verification Enabling Efficient Batch Verification

  • n Data Integrity for Cloud
  • n Data Integrity for Cloud

Chin‐Laung Lei Department of Electrical Engineering National Taiwan University

1

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Outline

 Introduction  System model  Protocol  Experiments and performance analysis  Conclusion

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Cloud Computing

Mobile Mobile Devices Devices

Users Users

Cloud Services Cloud Services Data Storage Data Storage

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Motivations

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Security for Cloud (Remote) Storage

 Confidentiality

 Various encryption systems

 Integrity

 Integrity verification protocols

 Availability

 Redundancy  Error correcting code

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Integrity Verification

 Message digest

 Naïve approach  No authenticated data integrity; Bandwidth wasting  Deterministic

 Provable data possession (PDP)

 Authenticated data integrity  Probabilistic

 Proof of retrivability (PoR)

 Authenticated data integrity & improved availability  Probabilistic

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Lifecycle

 Repository (data) deployment

 Generate tags

 Integrity verification

 Challenge data integrity  Generate proof of storage

 (Optional) Repository evolution

 Generate tags for modified part

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Scenario for Integrity Verification

Data Data Owner Owner Third Party Third Party Verifier Verifier Cloud Storage Provider

  • 1. Deploying Data
  • 2. Verifying Data

Integrity

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Issues

 Replay attack

 The status of repository is not clear

 Performance

 Slow verification  Even on personal computer

 Batch verification

 Single user  Multiple users

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Approaches

 Replay attack

 Revision number as timestamp

 Performance

 Multiplication instead of exponential operations

 Batch verification

 Repository as an single file

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Scenario for Single User

Data Owner Data Owner Repositories on the Cloud Repositories on the Cloud Third Party Verifier Third Party Verifier

Deployment, Modification, ... Batch Integrity Verification Integrity Verification

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Bilinear Map

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BLS Signatures

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Tokens

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Security Concern

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Security Concern

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Repository Deployment

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Repository Deployment

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Integrity Verification

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Integrity Verification

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Repository Evolution

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Batch Verification for Single User

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Probabilistic Detection

Number of blocks needed to fulfill certain detection rate under various data corruption rate

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Probabilistic Detection

 Check points

# of Challenged Blocks Detection Rate Data Corruption Rate 300 95% 1% 460 99% 1% 4610 99% 0.1% 6910 99.9% 0.1%

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Benchmarks

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Verification Time

Client-side verification time with 6910-block challenge

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Verification Time

Client-side verification time with 512-megabyte file

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Scenario for Multiple Users

Data Owner Data Owner Repository with Repository with Three Privilege Three Privilege Domains Domains Third Party Verifier Third Party Verifier

Deployment, Modification, ... Batch Integrity Verification Integrity Verification

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From Single User to Multiple Users

 Access control

 Who can commit modifications of a certain

part?

 Batch verification

 Verify integrity across different users’ data

 Race condition

 Concurrent write of the same project?

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Approaches

 Access control

 Multiple authority  Hierarchical

 Batch verification

 Repository as a single file

 Race condition

 Branching‐and‐merging

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Repository Deployment

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Key Delegation

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Tag Generation

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Integrity Verification

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Repository Evolution

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Batch Verification

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Branching-and-Merging

 Before modify shared data

 Copy to one’s own privilege domain

(branching)

 After finish the modification

 Coordinate with other collaborators  Write the modifications to the trunk (merging)

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Branching-and-Merging (Example)

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Conclusion

 Efficient integrity verification

 Can even run on smart phone!

 Batch verification

 Convenient for verifiers  Suitable for online co‐working

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Thank You

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Appendix