Differentially Private Query Verification With Vectorized NIZK Proofs
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Solution Overview
Problem
Existing methods for verifying differentially-private digital data are inefficient and impractical due to high time and memory complexity, making it difficult for readers and data subjects to verify the accuracy of query results without compromising privacy.
Innovation Solution
Implementing non-vectorized and vectorized aggregation query methods that generate proofs using homomorphic commitments, allowing clients to verify query results without accessing underlying data, with non-vectorized methods achieving linear scaling and vectorized methods achieving logarithmic scaling, reducing proof size and verification time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional zero-knowledge proof techniques are used to verify differentially-private statistics, then verification capability is provided, but time complexity and memory complexity become prohibitively high
Solution Approach 1:
The patent segments the verification process into two distinct components: (1) a proof generation phase performed by the data curator that creates cryptographic proofs of correct computation, and (2) a proof verification phase performed by readers that checks these proofs efficiently. This segmentation allows the computationally intensive generation to be done once, while verification can be performed quickly multiple times by multiple readers, resolving the contradiction between providing verification capability and maintaining low verification time complexity
Solution Approach 2:
The patent introduces cryptographic proofs as an intermediary mechanism between the data curator and readers. These proofs serve as verifiable evidence that statistics were computed correctly without requiring readers to reperform the computation or access raw data. This intermediary enables reliable verification while keeping the verification process efficient, as readers only need to check the cryptographic proofs rather than recalculate statistics
2Reliability
If traditional zero-knowledge proof techniques are used to verify differentially-private statistics, then verification capability is provided, but memory requirements become prohibitively high
Solution Approach 1:
The patent segments the verification burden by having the data curator generate and store cryptographic proofs separately from the raw data. These proofs are structured in a way that allows efficient verification with minimal memory usage by readers. The segmentation enables the system to provide verification capability while keeping the memory footprint manageable, as readers only need to load and verify the compact cryptographic proofs rather than handling large datasets
Solution Approach 2:
The patent uses cryptographic copies (proofs) of the computation process that can be verified without accessing the original raw data. These proof copies contain sufficient information to verify correctness but use minimal memory space compared to the full dataset. This copying approach allows verification capability while dramatically reducing memory requirements for the verification process
3Productivity
If data records are made accessible to data analysts for study, then analytical capability is improved, but privacy protection is weakened
Solution Approach 1:
The patent introduces differential privacy mechanisms as an intermediary layer between the data curator and data analysts. This intermediary adds carefully calibrated noise to the data or query results, enabling analysts to perform meaningful statistical analysis while preventing the extraction of information about individual records. The intermediary preserves analytical capability by maintaining the overall statistical properties of the data while protecting individual privacy through the noise addition
4Object-affected harmful factors
If noise values are added to statistics for differential privacy, then privacy protection is improved, but measurement precision is reduced
Solution Approach 1:
The patent employs parameter changes by adjusting the amount of noise added based on the sensitivity of the query and the desired privacy level (controlled by epsilon parameters). For less sensitive queries or when higher precision is needed, less noise is added. For more sensitive queries, more noise is added. This parameter adjustment allows the system to optimize the balance between privacy protection and measurement precision dynamically, rather than using a fixed noise level for all queries
Data Source
AI summary
Embodiments of the present disclosure enable users to efficiently verify digital data produced by queried databases, even when that data is differentially-private (e.g., satisfying the conditions of differential privacy in order to protect sensitive or private data). In addition to the query result, a database computer can provide the client with a non-interactive zero-knowledge proof (NIZK), data that the client can use to verify the digital data contained in the query result, without revealing any private data to the client. Various innovations, including vectorized proofs, enable the database computer to generate proofs that require less data (e.g., when measured in bytes) than most NIZK proof systems. Consequently, these proofs can be transmitted and verified more quickly and efficiently. Embodiments of the present disclosure can make use of partially or homomorphic commitments and efficient vector proof techniques to achieve these performance improvements.