Digital Proof Generation Using Commitments for Private Query Verification
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Solution Overview
Problem
Conventional verifiable computing systems fail to guarantee the accuracy and completeness of computational tasks executed on datasets without revealing the underlying data, especially when dealing with untrustworthy clients.
Innovation Solution
A system and method utilizing homomorphic hashing and zero-knowledge proofs to verify the execution of queries on datasets, ensuring correctness and completeness without disclosing the dataset, by using a commitment storage module, query execution module, and result verification module to manage and verify commitments associated with dataset operations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If computational tasks are outsourced to untrustworthy clients, then productivity is improved, but reliability deteriorates because the system cannot confirm accuracy of outsourced tasks
Solution Approach 1:
The system performs preliminary actions by generating cryptographic commitments (hash values) of the dataset before outsourcing computational tasks. These commitments are stored and later used for verification, allowing the system to prepare verification mechanisms in advance without delaying the outsourced computation
Solution Approach 2:
Cryptographic commitments act as an intermediary between the untrustworthy client and the verification system. The commitment (hash value) serves as a trusted reference point that mediates the verification process, allowing accuracy confirmation without requiring trust in the client or direct access to the original dataset
2Measurement precision
If the underlying dataset is revealed for verification, then measurement precision is improved, but security deteriorates due to data exposure
Solution Approach 1:
The invention extracts only the essential verification element (cryptographic commitment/hash value) from the dataset, separating it from the sensitive underlying data. This extracted commitment is used for verification purposes while the original dataset remains protected and undisclosed
Solution Approach 2:
Instead of using the original dataset for verification, the system uses a cryptographic copy (hash value/commitment) that preserves the verification functionality while eliminating security risks. This copy can be freely shared and used without exposing sensitive information
3Reliability
If cryptographic commitments are generated and stored for each data row, then reliability is improved, but device complexity increases
Solution Approach 1:
The verification system is segmented into distinct functional modules: a data management module for generating and storing commitments, a query execution module for processing tasks, and a result verification module for validating outcomes. This segmentation organizes complexity into manageable, independent components
Solution Approach 2:
The cryptographic commitment mechanism serves multiple functions within the system: it enables accuracy verification, ensures data integrity, provides audit trails, and supports various query types (selection, aggregation, update). This multi-functionality reduces the need for separate verification mechanisms for different operations
Data Source
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AI summary
A system and a method for digital proof generation are provided. The system includes a data management module to manage dataset having plurality of data rows and a query execution and verification module including a commitment storage sub-module, a query execution sub-module and a result verification sub-module. The data management module receives query from the query execution sub-module, related to operation on data rows of plurality of data rows, which is processed to generate execution result associated with data rows. The execution result is transferred along with data rows to the query execution sub- module. A set of commitments associated with execution result is transferred to the commitment storage sub-module. The query execution sub-module transfers set of commitments, from the commitment storage sub-module, and data rows to the result verification sub-module for verification, and receives a verification result from the result verification sub-module.