Differential Privacy Noise Generation with Adaptive Binomial Parameters
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Solution Overview
Problem
Existing differential privacy techniques face challenges in achieving accurate and efficient noise generation that balances privacy and utility, often requiring excessive computational resources and privacy budgets, limiting the number of queries that can be performed without risking data leaks.
Innovation Solution
The use of Almost Concentrated Differential Privacy (ACDP) and binomial distribution parameters to generate differential privacy noise, allowing for more flexible privacy budget allocation and reduced computational overhead, resulting in lower error rates and more accurate data output.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional differential privacy techniques are used to add noise to data, then privacy protection is achieved, but the accuracy and utility of the data deteriorates due to excessive noise
Solution Approach 1:
The patent changes the parameters of the noise distribution from fixed conventional values to dynamically optimized binomial distribution parameters (n, p) that are selected to satisfy differential privacy constraints while minimizing impact on data accuracy. This allows achieving the same privacy protection with less noise.
Solution Approach 2:
The patent introduces a dynamic parameter selection process where the binomial distribution parameters are chosen based on the specific query and data characteristics, rather than using static noise levels. This enables adaptive optimization of the privacy-accuracy tradeoff for different scenarios.
2Reliability
If conventional differential privacy noise generation is used, then privacy is protected, but computational resources are excessively consumed
Solution Approach 1:
The patent extracts and optimizes only the critical components of noise generation by focusing on selecting optimal binomial distribution parameters rather than implementing complex full differential privacy mechanisms. This reduces computational overhead while maintaining privacy guarantees.
Solution Approach 2:
By changing from conventional noise generation parameters to optimized binomial parameters (n, p), the patent achieves the same privacy protection with computationally more efficient operations, reducing the computational resources required.
3Reliability
If conventional differential privacy techniques are used, then privacy is maintained, but the number of queries that can be performed is limited due to excessive privacy budget consumption
Solution Approach 1:
The patent optimizes the privacy budget utilization by selecting binomial distribution parameters that achieve the required privacy protection with minimal noise addition. This more efficient use of the privacy budget allows performing more queries before exhausting the budget.
Solution Approach 2:
The patent applies just enough noise to satisfy differential privacy constraints without adding excessive noise. By carefully selecting parameters that meet the privacy requirement without over-provisioning, more query operations can be performed within the same privacy budget.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating differential privacy noise and applying the noise to data. In one aspect, a method includes obtaining a first binomial distribution parameter. Target differential privacy parameters representing a target level of differential privacy are obtained. The target differential privacy parameters include a first target differential privacy parameter representing a privacy metric that controls a level of privacy of data. For each value of multiple values of a second binomial distribution parameter, an actual value of a first actual differential privacy parameter that represents an actual privacy metric is determined based on the value of the second binomial distribution parameter. A determination is made whether the actual value of the first differential privacy parameter satisfies the first target differential privacy parameter. A selection is made of a given value of the second binomial distribution parameter.