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Hidano, S., Murakami, T., Katsumata, S., Kiyomoto, S. and Hanaoka, G. (2017) Model Inversion Attacks for Prediction Systems: Without Knowledge of Non-sensitive Attributes. 15th Annual Conference on Privacy, Security and Trust, Calgary, 28-30 August 2017, 115-126.
https://doi.org/10.1109/PST.2017.00023
https://dblp.uni-trier.de/rec/conf/pst/HidanoMKKH17.html?view=bibtex

has been cited by the following article:

• TITLE: Defense against Membership Inference Attack Applying Domain Adaptation with Addictive Noise

  AUTHORS: Hongwei Huang

  KEYWORDS: Privacy-Preserving Machine Learning, Membership Inference Attack, Domain Adaptation, Deep Learning

  JOURNAL NAME: Journal of Computer and Communications, Vol.9 No.5, May 28, 2021

  ABSTRACT: Deep learning can train models from a dataset to solve tasks. Although deep learning has attracted much interest owing to the excellent performance, security issues are gradually exposed. Deep learning may be prone to the membership inference attack, where the attacker can determine the membership of a given sample. In this paper, we propose a new defense mechanism against membership inference: NoiseDA. In our proposal, a model is not directly trained on a sensitive dataset to alleviate the threat of membership inference attack by leveraging domain adaptation. Besides, a module called Feature Crafter has been designed to reduce the necessary training dataset from 2 to 1, which creates features for domain adaptation training using noise addictive mechanisms. Our experiments have shown that, with the noises properly added by Feature Crafter, our proposal can reduce the success of membership inference with a controllable utility loss.