RAPID: Risk of Attribute Prediction-Induced Disclosure in Synthetic Microdata

TL;DR

This paper introduces RAPID, a new risk measure for synthetic microdata that quantifies an adversary's ability to infer sensitive attributes, providing a practical and interpretable disclosure risk assessment method.

Contribution

RAPID offers a novel, robust, and interpretable metric for attribute inference risk in synthetic data, independent of specific generators and applicable with various learning algorithms.

Findings

RAPID effectively quantifies attribute inference risk in synthetic data.

The metric is robust to class imbalance and independent of the synthesis method.

Empirical results demonstrate RAPID's utility in evaluating synthetic data privacy.

Abstract

Statistical data anonymization increasingly relies on fully synthetic microdata, for which classical identity disclosure measures are less informative than an adversary's ability to infer sensitive attributes from released data. We introduce RAPID (Risk of Attribute Prediction--Induced Disclosure), a disclosure risk measure that directly quantifies inferential vulnerability under a realistic attack model. An adversary trains a predictive model solely on the released synthetic data and applies it to real individuals' quasi-identifiers. For continuous sensitive attributes, RAPID reports the proportion of records whose predicted values fall within a specified relative error tolerance. For categorical attributes, we propose a baseline-normalized confidence score that measures how much more confident the attacker is about the true class than would be expected from class prevalence alone, and we summarize risk as the fraction of records exceeding a policy-defined threshold. This construction yields an interpretable, bounded risk metric that is robust to class imbalance, independent of any specific synthesizer, and applicable with arbitrary learning algorithms. We illustrate threshold calibration, uncertainty quantification, and comparative evaluation of synthetic data generators using simulations and real data. Our results show that RAPID provides a practical, attacker-realistic upper bound on attribute-inference disclosure risk that complements existing utility diagnostics and disclosure control frameworks.