Proof Chaining for Zero-Knowledge Proof Efficiency
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Solution Overview
Problem
Current zero-knowledge proof systems, such as zk-SNARK, face challenges with computational efficiency, particularly when dealing with large amounts of underlying data, and require regenerating proofs for new data points, which is burdensome and inefficient.
Innovation Solution
The introduction of proof chaining allows for the division of the proving process into multiple components that can be analyzed in series or parallel, using 'glue values' to maintain verification and attestation, enabling efficient generation and updating of proofs without exposing underlying data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional zk-SNARK proof generation is used for large amounts of data, then cryptographic security and privacy are maintained, but computational efficiency deteriorates and proof generation becomes burdensome
Solution Approach 1:
The patent divides the proving process into multiple independent components or stages, where each stage processes a portion of the data and generates an intermediate proof. These segmented proofs can then be combined to form a complete verification, allowing parallel processing and reducing the computational burden on any single system while maintaining the overall cryptographic security of the zero-knowledge proof mechanism.
2Reliability
If traditional zk-SNARK proof generation is used for large amounts of data, then cryptographic security and privacy are maintained, but proof generation time increases
Solution Approach 1:
The patent performs preliminary processing and preparation of data before the main proof generation process. This includes pre-computing certain cryptographic parameters, organizing data into optimal structures, and setting up intermediate verification states in advance. By preparing these elements beforehand, the actual proof generation time is reduced when new data needs to be verified, while the pre-computed elements maintain cryptographic security.
3Measurement precision
If proofs are regenerated for new data points, then verification accuracy is maintained, but the process becomes inefficient and burdensome
Solution Approach 1:
The patent enables continuous verification where proofs can be updated incrementally as new data points arrive, rather than requiring complete regeneration. The segmented proof structure allows new data to be processed and integrated into existing proof chains, maintaining verification accuracy through cumulative validation while significantly improving efficiency by avoiding redundant computation on previously verified data.
4Reliability
If complete proofs are generated for all data, then verification integrity is ensured, but memory space and processing resources are excessively consumed
Solution Approach 1:
The patent extracts and separates the essential verification elements from the complete data set. Instead of storing and processing entire proofs for all data, the system identifies and extracts only the critical cryptographic components needed for verification, such as commitment values and proof fragments. This extraction maintains verification integrity while dramatically reducing the memory space and computational resources required, as only the essential proof elements are retained rather than complete proof structures for all data.
Data Source
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AI summary
Disclosed herein are system, method, and computer program product embodiments for generating and chaining zero-knowledge proofs ("ZKPs") using proof chaining. A proof may be divided into different components to analyze data in a series and/or parallel manner using chained proof systems. The proof systems may generate hash values indicating that particular functions were applied to the instances of the underlying data. The hash values may prove that a particular function was applied to instances of the underlying data. Using the proof chaining configuration, a data supply system may generate a ZKP allowing a data requesting system to trust a function result without needing to receive the underlying data.