Dynamic Cloud Storage Proof of Retrievability via Block Segmentation
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Solution Overview
Problem
Conventional systems for cloud storage lack flexibility and efficiency in dynamic updates, such as partial writes, inserting, or removing blocks, and existing dynamic proofs of retrievability incur significant computational overhead.
Innovation Solution
A method that divides information into chunks and blocks, computes tags using a random function and index function, allowing for efficient dynamic updates by shifting blocks and updating tags, enabling users to verify retrievability with minimal computational expense.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional cloud storage systems are used, then data storage is provided, but flexibility and efficiency in dynamic updates (partial writes, inserting, or removing blocks) are lacking
Solution Approach 1:
The information is divided into multiple chunks, and each chunk is further divided into multiple blocks. This segmentation allows independent manipulation of individual blocks without affecting the entire data structure, enabling flexible dynamic updates such as partial writes, insertions, and deletions while maintaining overall system integrity.
2Reliability
If existing dynamic proofs of retrievability are implemented, then data integrity verification is enabled, but significant computational overhead is incurred
Solution Approach 1:
Tags for each block are pre-computed using a random function and index function during the initial data setup phase. These pre-computed tags are stored alongside the data blocks. During verification, the system only needs to perform simple tag comparisons rather than complex computational checks, significantly reducing the computational overhead while maintaining reliable data integrity verification.
3Productivity
If data is divided into chunks and blocks with computed tags, then efficient dynamic updates are enabled, but computational overhead for tag computation is introduced
Solution Approach 1:
The tags for each block are pre-computed and stored during the initial data ingestion phase. When dynamic updates occur, the system leverages these pre-computed tags and only performs minimal modifications or comparisons, rather than re-computing tags from scratch. This approach significantly reduces the computational time required for dynamic updates while maintaining data integrity.
Data Source
AI summary
A method for storing information includes receiving information to be stored and an information tag from a user computing entity, and storing the information and the information tag. The information to be stored includes a chunk that is divided into a plurality of blocks each comprising one or more elements. The information tag comprises a plurality of tags each having been computed for one of the blocks, wherein the tag for the j-th block of the i-th chunk is based on: an output of a random function using as input: 1) an output of an index function mapping each index j to a certain value, and/or 2) a seed sampled for the i-th chunk; the j-th block; and a representation of a second secret comprising one or more random elements each associated with a respective one of the one or more elements of one of the blocks.