Adaptive Privacy Budgeting for Accurate Noisy Value Prediction
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Solution Overview
Problem
Existing methods for tracking user interactions and estimating counts lack differential privacy, potentially revealing sensitive user information, and struggle to provide accurate estimates within a target accuracy threshold.
Innovation Solution
A method involving the generation of multiple noisy datasets by adding random noise to ground truth values, processing these datasets to determine a predicted value, and stopping when the standard deviation meets a threshold, ensuring differential privacy and target accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If noisy datasets are generated by adding random noise to ground truth values to maintain differential privacy, then user privacy is protected, but measurement precision deteriorates due to noise contamination
Solution Approach 1:
The patent segments the estimation process into multiple independent rounds, each generating noisy estimates with differential privacy. By dividing the overall estimation task into multiple stages with progressively refined noise levels, the system achieves both privacy protection and improved accuracy through aggregated results across rounds.
Solution Approach 2:
The patent implements dynamic adaptation of noise parameters and sampling rates across different rounds of estimation. The noise standard deviation and sampling rate are adjusted based on previous round results, allowing the system to optimize the balance between privacy protection and measurement precision adaptively rather than using fixed parameters.
2Measurement precision
If multiple rounds of noisy value generation are performed to improve estimation accuracy, then measurement precision improves, but loss of time increases due to iterative processing
Solution Approach 1:
The patent incorporates feedback mechanisms where each round's noisy estimates and standard deviations are used to inform subsequent rounds. The system monitors the reduction in standard deviation across rounds and uses this feedback to determine when to terminate, preventing unnecessary additional rounds and optimizing the time-accuracy tradeoff.
Solution Approach 2:
The patent performs a predetermined number of rounds that is sufficient to achieve the target accuracy threshold but not excessively more. By carefully selecting the number of rounds based on expected convergence behavior, the system avoids both insufficient processing and wasteful over-processing, achieving optimal efficiency.
3Reliability
If the noise range is increased to enhance privacy protection, then differential privacy is strengthened, but manufacturing precision deteriorates due to larger estimation errors
Solution Approach 1:
The patent dynamically adjusts the noise range (standard deviation) across different rounds of estimation. Early rounds use larger noise ranges for stronger privacy protection, while subsequent rounds use progressively smaller noise ranges to refine accuracy. This dynamic adjustment allows the system to achieve both strong privacy protection and high estimation precision.
Solution Approach 2:
The patent segments the privacy-precision tradeoff across multiple rounds, with each round serving a different purpose in the overall estimation process. By dividing the task into stages with differentiated noise levels, the system achieves cumulative accuracy improvement while maintaining strong privacy protection through the aggregation of multiple noisy observations.
4Productivity
If adaptive sampling with varying sampling rates is used to improve efficiency, then productivity increases, but device complexity increases due to additional control mechanisms
Solution Approach 1:
The patent implements adaptive sampling where the sampling rate dynamically changes across rounds based on the estimated variance and target accuracy requirements. Later rounds use higher sampling rates to refine estimates with smaller noise, while earlier rounds use lower sampling rates. This dynamic adaptation improves efficiency without requiring complex per-sample decision mechanisms.
Solution Approach 2:
The patent changes the sampling rate parameter across different rounds of estimation based on performance requirements and observed variance. By adjusting this single parameter systematically, the system achieves improved productivity without introducing complex control logic, maintaining relative simplicity while adapting to accuracy requirements.
Data Source
AI summary
Systems and methods for generating and maintaining differential privacy while providing accurate values can include obtaining a plurality of noise-added values, processing the plurality of noise-added values to determine a predicted value. The plurality of noise-added value may be utilized to determine one or more accuracy values that can be compared to a threshold to determine if more data is to be obtained and processed before providing a predicted value.