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

2026-02-09

AI Summary

This work proposes RAPID, a novel attribute inference risk metric designed to better reflect realistic attack scenarios in synthetic microdata, where an adversary trains a predictive model on the synthetic dataset and attempts to infer sensitive attributes of real individuals using their quasi-identifiers. RAPID is the first bounded, interpretable risk measure that aligns with attacker capabilities, is independent of the synthesis mechanism, compatible with any learning algorithm, and robust to class imbalance. It handles continuous attributes via relative error tolerance, evaluates categorical attributes through baseline-normalized confidence scores, and integrates threshold calibration with uncertainty quantification. Experimental results demonstrate that RAPID provides tight, practical upper bounds on disclosure risk, effectively complementing existing privacy–utility evaluation frameworks.

Technology Category

• Machine Learning: Privacy
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty
• Natural Language Processing: Safety and Robustness

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

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.

Problem

• synthetic microdata
• disclosure risk
• attribute inference
• data anonymization
• privacy

Innovation

• synthetic microdata
• disclosure risk
• attribute inference
• RAPID
• privacy evaluation