Dynamic Noise Parameterization for Differential Privacy Trade-offs
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Solution Overview
Problem
Existing differential privacy systems do not allow data analysts to input their utility requirements, resulting in a fixed privacy-utility trade-off that does not consider the data analyst's needs.
Innovation Solution
The proposed system, Randomly Parameterizing Differentially Private mechanisms (R2DP), introduces a dynamic utility input mechanism that considers both privacy constraints from the data owner and utility constraints from the data analyst, optimizing the privacy-utility trade-off by applying a second distribution on the noise parameter.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If noise is added to query results to achieve differential privacy guarantees, then privacy protection is improved, but utility (accuracy) of the results deteriorates
Solution Approach 1:
The patent applies dynamics by making the noise parameter randomizable rather than fixed. The system allows the noise parameter to be dynamically adjusted based on utility requirements input by data analysts, transforming a static differential privacy mechanism into a dynamic one that can adapt to different utility-privacy tradeoff preferences.
Solution Approach 2:
The patent changes the parameter of the noise distribution by introducing randomization. Instead of using a fixed noise parameter determined solely by the data owner's privacy constraint, the system randomly selects from a distribution of noise parameters, allowing the actual noise level to vary and potentially achieve better utility while maintaining privacy guarantees.
2Reliability
If fixed noise parameters are used in differential privacy mechanisms, then privacy constraints are satisfied, but utility requirements from data analysts cannot be optimized
Solution Approach 1:
The system transforms the static noise parameter into a dynamic variable that can be adjusted based on utility requirements. Data analysts can input their utility preferences, and the system dynamically selects appropriate noise parameters from a distribution, enabling adaptation to different analytical needs while maintaining privacy guarantees.
Solution Approach 2:
The patent adds another dimension to the parameter space by moving from fixed noise parameters to randomizable noise parameters with distributions. This dimensional expansion allows the system to simultaneously satisfy privacy constraints and optimize for utility requirements, creating a more flexible search space for optimal parameters.
3Reliability
If noise parameter is fixed based on data owner's privacy constraint, then privacy guarantee is maintained, but data analyst's utility input is not considered
Solution Approach 1:
The patent implements feedback by incorporating data analyst's utility requirements into the noise parameter selection process. The system takes utility inputs from data analysts, uses this feedback to determine appropriate noise parameters from a distribution, and generates query results that balance both privacy and utility considerations.
Solution Approach 2:
The system makes the noise parameter dynamic by allowing it to be adjusted based on feedback from data analysts. Instead of a fixed parameter determined solely by the data owner, the noise parameter can now adapt to utility requirements, making the system more responsive to the needs of different users.
Data Source
AI summary
A method, system and apparatus are disclosed. In one or more embodiments, a differential privacy, DP, node is provided. The DP node includes processing circuitry configured to: receive a query request; receive a first input corresponding to a utility parameter; receive a second input corresponding to a privacy parameter; select a baseline DP mechanism type based at least on a query request type of the query request, the first input and the second input, where the baseline DP mechanism type includes at least a noise parameter; generate a noise distribution based on the baseline DP mechanism type using a first value of the noise parameter; and determine a DP query result based on applying the noise distribution to the query request applied on a data set.