Summary Chain Hash Block Segmentation
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Solution Overview
Problem
The growth of hash chains indefinitely due to their modification-resistant nature makes verifying and auditing data stored on them increasingly time-consuming and resource-intensive, necessitating improved techniques for data summarization and analysis.
Innovation Solution
A method for summarizing data in distributed systems by generating an ordered list of blocks from a hash chain, applying a summary function to these blocks to create summary data, and adding this data to a summary chain, allowing for periodic summarization and efficient analysis and auditing without traversing the entire hash chain.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If hash chains are used to store data with modification-resistant properties, then data integrity and security are improved, but verification and auditing time increase indefinitely
Solution Approach 1:
The patent segments the hash chain into fixed-size windows and creates summary blocks for each window. Each summary block contains aggregated data (e.g., sum, count, minimum, maximum) of the values within that window, allowing verification without traversing every individual block. This segmentation resolves the contradiction by maintaining data integrity through cryptographic hashing while reducing verification time through aggregated summaries.
Solution Approach 2:
The patent performs preliminary summarization of hash chain blocks into summary blocks that pre-compute aggregate statistics. These summary blocks are created in advance and stored with the hash chain, enabling faster verification and auditing operations. The preliminary action of creating summary structures resolves the time increase problem while preserving the reliability benefits of hash chains.
2Reliability
If hash chains grow indefinitely to maintain secure records, then audit trail completeness is improved, but processing and memory resources increase
Solution Approach 1:
The patent divides the indefinitely growing hash chain into fixed-size windows and creates summary blocks for each window. Each summary block stores aggregated statistics (sum, count, min, max) of the values within that window. This segmentation allows the system to maintain complete audit trails while reducing processing and memory resources by working with compact summary representations rather than individual block data.
Solution Approach 2:
The patent computes and stores only partial information (aggregate statistics) in summary blocks rather than complete data. By storing summarized metrics like sum, count, minimum, and maximum values instead of all individual data points, the system maintains audit trail completeness for verification purposes while significantly reducing the processing and memory resources required.
3Measurement precision
If verification traverses each block of the hash chain, then verification accuracy is improved, but resource consumption increases
Solution Approach 1:
The patent segments the hash chain into windows with corresponding summary blocks. Verification can operate at the summary block level using pre-computed aggregates, providing accurate verification of data properties without traversing every individual block. This segmentation maintains verification accuracy while reducing resource consumption by an order of magnitude compared to block-by-block verification.
Solution Approach 2:
The patent creates summary blocks that copy and aggregate essential information from multiple hash chain blocks. These summary blocks serve as verified representations of the underlying data, allowing accurate verification operations to be performed on the compact summary structures rather than the full set of individual blocks, thereby reducing resource consumption while maintaining verification accuracy.
Data Source
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AI summary
Certain aspects of the present disclosure provide techniques for summarizing data in a distributed system. Embodiments include generating an ordered list of blocks by iterating through a first group of blocks of a hash chain starting at a last block of the hash chain and adding each of the first group of blocks of the hash chain to the ordered list. Embodiments further include generating summary data by applying a summary function to the first group of blocks based on the ordered list. The summary function may take the ordered list as an input and provide the summary data of the first group of blocks as an output. Embodiments further include generating a summary block comprising the summary data and adding the summary block to a summary chain.