PPIA-MTL: Efficient Property Proportion Inference Attacks on Tabular Generative Models via Multi-Task Learning

Ninghui Zhang, Chun Long, Jing Li, Fan Yang, Yuhao Fu, Wei Wan, Haojie Nie, Xingbo Pan

Abstract: While generative models for tabular data offer strong capabilities in data synthesis, they also raise serious privacy concerns. Property Proportion Inference Attacks (PPIAs) pose a critical threat by aiming to infer the distribution of sensitive attributes in the training data. Existing approaches often struggle with low efficiency and limited adaptability in high-dimensional tabular settings.In this paper, we propose PPIA-MTL, a novel attack framework based on Multi-Task Learning (MTL) that enables efficient parallel inference of multiple attributes, significantly reducing computational cost. We further extend the framework to support continuous attributes and introduce a unified evaluation metric, CACRS (Continuous Attribute Comprehensive Reasoning Score), which comprehensively assesses inference performance from the perspectives of distributional consistency, numerical error, and more.Experiments on real-world datasets show that PPIA-MTL achieves a minimum MAE of 1.55% on binary attributes and improves inference accuracy for continuous attributes by up to 15.5 over existing methods. As the number of inference tasks increases, training cost is reduced by more than 10×. Finally, we apply PPIA-MTL as a privacy auditing tool and find that some diffusion models exhibit lower inference risk while maintaining high utility, demonstrating promising potential for balancing privacy and utility under the Group-Level Statistical Privacy Risk (GSPR).

External IDs:dblp:conf/trustcom/ZhangLLYFWNP25