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Remote Data Integrity For Cloud Based Storage System

Remote Data Integrity For Cloud Based Storage System
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Cloud computing provides a reliable and robust infrastructure for users to remotely store and access huge amount of data. However, data integrity in the cloud is a major security concern for data owners no longer physically possess their sensitive data. To mitigate this challenge, remote data integrity has been proposed as a mechanism to enable data owners to verify the correctness of their outsourced data. The remote verification process needs to be done with reduced communication, computation, and storage overhead. 

Product Number: 51216-010-SG
Author: Sravanthi Sukireddy, Ayad Barsoum
Publication Date: 2016
Industry: Coatings
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$20.00
$20.00

Cloud computing provides a reliable and robust infrastructure for users to remotely store and access huge amount of data. However, data integrity in the cloud is a major security concern for data owners no longer physically possess their sensitive data. To mitigate this challenge, remote data integrity has been proposed as a mechanism to enable data owners to verify the correctness of their outsourced data. The remote verification process needs to be done with reduced communication, computation and storage overhead. That is why traditional cryptographic primitives for data integrity based on hashing and signature schemes are not applicable; for it is impractical to download all stored data to validate their integrity (expensive I/O operations and immense communication overheads).Therefore, provable data possession (PDP) has been the main focus for many research studies to efficiently, periodically, and securely validate that a remote server –which supposedly stores the owner's potentially very large amount of data –is actually storing the data intact. There are many different variations of PDP schemes under different cryptographic assumptions. In this study, we provide a comparative analysis of various PDP schemes. We investigate not only PDP schemes for static data, but also protocols that handle the dynamic behavior of outsourced data. We implement a prototype that allows the data owner to outsource their data, and dynamically update the data by inserting, deleting, or modifying some data blocks. The prototype also evaluates the performance of different PDP schemes from different perspectives such as pre-computation times, computation times, verification times and storage overhead.

Cloud computing provides a reliable and robust infrastructure for users to remotely store and access huge amount of data. However, data integrity in the cloud is a major security concern for data owners no longer physically possess their sensitive data. To mitigate this challenge, remote data integrity has been proposed as a mechanism to enable data owners to verify the correctness of their outsourced data. The remote verification process needs to be done with reduced communication, computation and storage overhead. That is why traditional cryptographic primitives for data integrity based on hashing and signature schemes are not applicable; for it is impractical to download all stored data to validate their integrity (expensive I/O operations and immense communication overheads).Therefore, provable data possession (PDP) has been the main focus for many research studies to efficiently, periodically, and securely validate that a remote server –which supposedly stores the owner's potentially very large amount of data –is actually storing the data intact. There are many different variations of PDP schemes under different cryptographic assumptions. In this study, we provide a comparative analysis of various PDP schemes. We investigate not only PDP schemes for static data, but also protocols that handle the dynamic behavior of outsourced data. We implement a prototype that allows the data owner to outsource their data, and dynamically update the data by inserting, deleting, or modifying some data blocks. The prototype also evaluates the performance of different PDP schemes from different perspectives such as pre-computation times, computation times, verification times and storage overhead.

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