Cryptographic Rich Query Validation with Column-Level Merkle Proofs
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Solution Overview
Problem
Existing data models, such as relational and document models, face challenges when mapped to immutable key-value stores, as they struggle to provide cryptographic proofs at the column or attribute level without requiring the complete data record, limiting the ability to validate data integrity and authenticity.
Innovation Solution
The implementation of a Merkle Hash Tree (MHT) within an immutable key-value store, where data records are split into labeled components corresponding to columns or attributes, allowing cryptographic proofs to be generated and validated without needing the entire data record, by calculating hash trees over split values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cryptographic proofs are generated for complete data records, then data integrity validation is ensured, but data retrieval complexity and time increase
Solution Approach 1:
The patent segments data records into individual columns, creating separate cryptographic proofs for each column rather than requiring proofs for entire records. This allows validators to request and verify only the specific columns needed for their validation needs, significantly reducing the amount of data that must be retrieved and processed while maintaining cryptographic integrity guarantees.
2Reliability
If cryptographic proofs include all data fields, then complete data authenticity is verified, but proof data size and processing overhead increase
Solution Approach 1:
The patent divides cryptographic proofs into column-specific segments, where each column has its own proof structure containing only the hash values and metadata necessary to verify that column's integrity. This segmentation reduces the size of individual proofs and allows validators to process only the relevant column proofs needed for their specific validation requirements.
Solution Approach 2:
The patent extracts only the essential cryptographic elements (hash values, column identifiers, and minimal metadata) needed for validation, discarding unnecessary data from the complete record. This extraction process creates streamlined proofs that maintain authenticity verification capabilities while significantly reducing proof data size and processing complexity.
3Adaptability or versatility
If column-level cryptographic proofs are implemented, then selective data validation is enabled, but data model complexity increases
Solution Approach 1:
The patent segments the data model into columns with associated cryptographic metadata, where each column maintains its own proof structure. This segmentation enables selective validation by allowing systems to choose which column proofs to verify based on their specific needs, while the modular structure keeps the complexity manageable through consistent proof generation and validation patterns across all columns.
Data Source
AI summary
Disclosed are a computer-hosted database system providing cryptographic verifiability and comprising an immutable key-value data store and cryptographic proof data (which may be stored in a Merkle Hash Tree). The logical structure of a data record is defined by a rich data model and comprises a first field and a second field. Disclosed is a method that comprises receiving an instruction requesting retrieval of first field data and returning the data stored in the first field and cryptographic proof data sufficient to prove data originality of the first field data without knowledge of the data in the second field. The rich data model may be a relational data model or a document data model.