Pairwise Differential Privacy for Correlated Variable Utility
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Solution Overview
Problem
Existing methods for applying differential privacy to data sets fail to effectively preserve the utility of intercorrelated variables, leading to excessive data distortion and loss of privacy.
Innovation Solution
A pairwise differential privacy method that utilizes an adaptive sensitivity parameter and noise generation manager to apply additive noise based on correlations between variables, preserving privacy while maintaining data utility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If ε=1 differential privacy is applied to data set variables, then privacy protection is improved, but data utility deteriorates due to excessive distortion
Solution Approach 1:
The patent changes the privacy parameter from strict ε=1 differential privacy to (ε, Δ) differential privacy with ε=2 and Δ=0.0001, allowing for less noisy data while maintaining acceptable privacy guarantees. This parameter adjustment resolves the contradiction by finding a balance point where privacy protection remains strong but data utility is preserved.
Solution Approach 2:
The patent applies different noise levels to different variables based on their correlation structure. Variables that are highly correlated receive coordinated noise treatment to preserve relationships, while less correlated variables receive standard treatment. This local differentiation allows privacy protection to be applied selectively without uniformly degrading all data utility.
2Loss of information
If (ε, Δ) differential privacy with relaxed parameters is used, then data utility is improved, but privacy protection deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where the system evaluates the correlation structure of the data and adjusts noise application accordingly. The correlation manager analyzes variable relationships and provides feedback to the noise generation manager, which then applies appropriate noise levels. This feedback loop ensures that privacy protection remains adequate while maximizing data utility.
Solution Approach 2:
The patent makes the privacy mechanism dynamic by adapting noise application based on the specific characteristics of each variable and its correlations. Rather than applying uniform noise, the system dynamically adjusts the amount and type of noise based on real-time analysis of the data structure, allowing optimal balance between privacy and utility for each variable.
3Ease of operation
If standard differential privacy is applied without considering variable correlations, then processing simplicity is improved, but intercorrelation preservation deteriorates
Solution Approach 1:
The patent segments the data processing into distinct functional modules: a correlation manager that identifies variable relationships, and a noise generation manager that applies noise based on those relationships. This segmentation allows the complex task of correlation-preserving differential privacy to be broken into manageable steps, maintaining ease of operation while preserving intercorrelations.
Solution Approach 2:
The patent introduces a correlation manager as an intermediary between the data and the noise generation process. This intermediary analyzes the correlation structure and translates it into appropriate noise application strategies. The intermediary layer preserves the simplicity of the overall system while enabling sophisticated correlation-aware processing.
Data Source
AI summary
A method for applying pairwise differential privacy to variables in a data set is disclosed. The method includes designating a random instance seed value to a first data set variable in an original data set and designating the random instance seed value to at least one additional data set variable in the original data set if a high degree of correlation is identified between the first data set variable and the at least one additional data set variable. The method further includes determining an adaptive sensitivity parameter corresponding to the first data set variable and utilizing, by a noise generation manager, two or more among the first data set variable, the random instance seed value, and/or the adaptive sensitivity parameter to generate and apply additive noise to the first data set variable to produce a pseudonymized variable for inclusion in a pseudonymized data set associated with the original data set.