Interpretability Framework for Differential Privacy Parameters
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Solution Overview
Problem
Existing methods for interpreting and choosing differential privacy (DP) parameters (ε, δ) are cumbersome, especially for non-experts, and struggle to effectively communicate re-identification risk and plausible deniability.
Innovation Solution
A framework is developed to calculate privacy parameters ε, δ based on a specified adversarial posterior belief pc, which governs a differential privacy algorithm. This framework uses Gaussian or Laplacian mechanisms to perturb function outputs, ensuring that the posterior belief remains within the specified bounds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If differential privacy parameters (ε, δ) are chosen without an interpretability framework, then privacy protection can be implemented, but it becomes cumbersome for non-experts to interpret and choose parameters, and communication of re-identification risk is difficult
Solution Approach 1:
The patent introduces an interpretability framework that acts as an intermediary between the differential privacy parameters (ε, δ) and the end-user. This framework translates abstract privacy parameters into concrete, interpretable metrics such as re-identification risk and plausible deniability, making it easier for non-experts to understand and communicate privacy guarantees without losing the mathematical rigor of the original DP definition.
2Ease of operation
If existing interpretation approaches are used, then some communication of privacy guarantees is possible, but they stray from the original DP definition by offering upper bounds on privacy in face of an adversary with arbitrary auxiliary knowledge
Solution Approach 1:
The patent transforms the interpretation of differential privacy parameters by changing the parameter space from abstract (ε, δ) values to concrete security metrics. By defining new parameters such as re-identification risk and plausible deniability that are directly derived from the original DP definition, the framework maintains mathematical accuracy while improving communicability. The framework provides exact calculations rather than loose upper bounds, ensuring reliability of privacy guarantees.
Data Source
AI summary
Data is received that specifies a bound for an adversarial posterior belief pc that corresponds to a likelihood to re-identify data points from the dataset based on a differentially private function output. Privacy parameters ε, δ are then calculated based on the received data that govern a differential privacy (DP) algorithm to be applied to a function to be evaluated over a dataset. The calculating is based on a ratio of probabilities distributions of different observations, which are bound by the posterior belief pc as applied to a dataset. The calculated privacy parameters are then used to apply the DP algorithm to the function over the dataset. Related apparatus, systems, techniques and articles are also described.