Privacy-Preserving Common Data Intersection with Sampling and Noise
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Solution Overview
Problem
Existing private data intersection (PSI) solutions fail to calculate an intersection result while protecting privacy, as explicit disclosure is prohibited by privacy laws or regulations, and existing cryptographic methods are costly and inefficient.
Innovation Solution
Implement differential privacy by introducing noise into the intersection process through downsampling and upsampling, ensuring uncertainty in the intersection result, allowing statistical calculations without explicit data disclosure.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cryptographic PSI methods are used to protect privacy, then data privacy is protected, but calculation efficiency and cost are reduced
Solution Approach 1:
The patent extracts only the necessary intersection information from the data sets without revealing the complete data sets themselves. By using sampling techniques, the system extracts a representative subset of data for intersection calculation, reducing the computational burden while maintaining privacy protection through differential privacy mechanisms.
Solution Approach 2:
The patent changes the parameter of data representation from complete data sets to sampled data subsets. By adjusting the sampling rate and adding differential privacy noise, the system transforms the intersection calculation into a more efficient process that maintains privacy guarantees while improving calculation efficiency.
2Loss of information
If complete intersection results are disclosed to parties, then data utility is improved, but privacy protection is compromised
Solution Approach 1:
The patent applies partial action by providing only a sampled and noisy version of the intersection result rather than the complete exact intersection. This partial disclosure maintains sufficient data utility for statistical analysis while protecting privacy by preventing parties from inferring individual data points from the aggregated results.
Solution Approach 2:
The patent introduces differential privacy noise as an intermediary between the true intersection result and the disclosed result. This intermediary layer preserves the statistical properties needed for data utility while obscuring individual contributions, thereby protecting privacy without completely losing information.
3Measurement precision
If sampling rate is increased to improve accuracy, then intersection precision is improved, but communication cost and calculation complexity increase
Solution Approach 1:
The patent performs preliminary sampling and differential privacy noise addition before the intersection calculation. By pre-processing the data with appropriate sampling rates and noise levels, the system determines the optimal balance between precision and complexity in advance, avoiding the need for complex iterative adjustments during the intersection computation.
Data Source
AI summary
Methods, apparatuses, and systems for protecting privacy are described. In an example, a first party encrypts a first data set, and sends a single-encrypted full data set to a second party. The second party re-encrypts the single-encrypted full data set to obtain a double-encrypted full data set, obtains a single-encrypted sampling data set, and sends the single-encrypted sampling data set to the first party. The first party encrypts the single-encrypted sampling data set to obtain a double-encrypted sampling data set, determines an intersection of the double-encrypted full data set and the double-encrypted sampling data set, performs upsampling on a complement other than the intersection in the double-encrypted sampling data set to obtain a noise-added intersection, and sends an element index of the noise-added intersection to the second party. The second party determines related information of common data based on the element index.