Querying little is enough: Model inversion attack via latent information. Issue 2 (29th October 2020)

• Record Type: Journal Article
• Authors: Mo, Kanghua; Liu, Xiaozhang; Huang, Teng; Yan, Anli
• Is Part Of: International journal of intelligent systems. Volume 36:Issue 2(2021)
• Page Start: 681
• Page End: 690
• Publication Date: 2020-10-29
• Subjects: latent information -- machine learning -- model inversion attack -- privacy‐preserving -- query times; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; 006.3
• DOI: 10.1002/int.22315
• Languages: English
• ISSNs: 0884-8173

Abstract

Abstract: As machine learning (ML) technologies evolve, various online intelligent services use ML models to provide predictions. Unfortunately, attackers can obtain the private information of the model by interacting with the online service, namely model inversion attack (MIA). However, MIA requires large data sets to be transferred to an online service to obtain the predictive value of the inference model. Besides, the huge transmission may cause the administrator's active defense. To overcome this drawback, we propose a novel MIA scheme, which leverages latent information extracted by an auxiliary neural network as high‐dimensional features to simplify what inversion model should learn. The core idea of our scheme is to reuse some parameters of the local pretraining model. Extensive experiments have verified the effectiveness of our method in convolutional neural networks on LFW, pubFig, MNIST data sets. Experimental results show that even with a few queries, our inversion method still work accurately and is superior to other technologies. It is worth mentioning that our method makes it more difficult for administrators to defend against the attack and elicit more investigations for privacy‐preserving.