Differential Privacy Noise Calibration for Accurate Query Results
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Solution Overview
Problem
Existing differential privacy protocols and algorithms for data security in interactive user queries apply static noise levels, leading to either overly noisy results or insufficient privacy protection, failing to balance data privacy and accuracy.
Innovation Solution
Dynamic calibration of noise parameters based on query result magnitude and user-specified error tolerance using differential privacy protocols, adjusting epsilon and delta to generate noise-laden query results that meet specified accuracy requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If static noise levels are applied to query results, then data privacy protection is provided, but data accuracy deteriorates due to overly noisy results
Solution Approach 1:
The patent transforms the static noise application mechanism into a dynamic one by continuously monitoring query result characteristics (magnitude, variance, sensitivity) and adjusting noise parameters (epsilon, delta, noise scale) in real-time. This allows the system to adapt noise levels to the specific characteristics of each query result, providing strong privacy protection when needed while maintaining accuracy when possible.
Solution Approach 2:
The patent systematically changes key parameters including noise scale, epsilon (privacy budget), delta (failure probability), and sensitivity thresholds based on query result analysis. By dynamically adjusting these parameters rather than using fixed values, the system optimizes the balance between privacy protection and data accuracy for different query scenarios.
2Reliability
If higher noise levels are added to query results, then data privacy protection is improved, but data utility deteriorates
Solution Approach 1:
The patent applies different noise levels to different query results based on their local characteristics such as magnitude, variance, and sensitivity. High-value or low-sensitivity queries receive minimal noise to preserve utility, while low-value or high-sensitivity queries receive stronger noise for privacy protection. This localized approach ensures each query result receives appropriately tailored privacy protection.
Solution Approach 2:
The patent applies noise selectively rather than uniformly - using partial action by applying strong noise only when necessary for privacy protection, and minimal or no noise when privacy risk is low. This avoids excessive noise application that would unnecessarily degrade data utility across all queries.
3Measurement precision
If dynamic noise calibration is applied, then data accuracy is improved, but system complexity increases
Solution Approach 1:
The patent performs preliminary analysis of query results (calculating magnitude, variance, sensitivity metrics) before applying noise, and uses pre-defined thresholds and rules for noise parameter selection. This preliminary characterization simplifies the subsequent noise application decision-making process, making the dynamic system more manageable despite its complexity.
Solution Approach 2:
The patent implements feedback loops where query result characteristics inform noise parameter selection, and the effectiveness of noise application is monitored to adjust future noise levels. This feedback mechanism enables automated adaptation without requiring complex manual configuration, managing system complexity through self-regulation.
Data Source
AI summary
Data security for a dataset in interactive query or operation from users regarding data stored in the dataset is provided. A method for providing data security for the dataset in secure data computation and communication includes generating a query result corresponding to the user query for the dataset, determining a magnitude range of the query result, and generating an amount of random noise data based on the magnitude range. The amount of random noise data is calibrated by adjusting at least one of a first tunable parameter within a first range and a second tunable parameter within a second range of a differential privacy (DP) configuration. A noise-laden query result is generated by applying the noise data to the query result to satisfy an error tolerance level.