Querying Little Is Enough: Model Inversion Attack via Latent Information

Abstract

With the development of machine learning (ML) technology, various online intelligent services use ML models to provide predictions. However, attacker may obtain privacy information of the model through interaction with online services. Model inversion attacks (MIA) is a privacy stealing method that utilizes ML models output values to reconstruct input values. In particular, an indispensable step of implementing proposed MIA approaches is that the attacker query the auxiliary datasets entirely. However, in reality, it will be inefficient to transfer huge datasets to online services to get prediction values of inference models. More seriously, the huge transmission may cause the administrator’s active defense. In this paper, we propose a novel MIA scheme which reduce queries on auxiliary datasets, by utilizing latent information of primitive models as high dimension features. We systematically evaluate our inversion approach in convolutional neural networks (CNN) classifier on LFW, pubFig, MNIST datasets. The experimental results show that even with a few queries of the inference model, our inversion approach still work accurately and outperforms than previous approaches. As conclusion, our method proves that implementing MIA does not require querying all auxiliary data on the classifier model, making it more difficult for the administrator to defend against the attack and elicit more investigations for privacy-preserving.