From 6b95a00e8c81b74dbba1c6325d568e201d1b403a Mon Sep 17 00:00:00 2001 From: Suha Sabi Hussain Date: Thu, 16 Jul 2020 23:21:42 -0400 Subject: [PATCH] Add more inversion papers --- README.md | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 4de6234..dc4cd82 100644 --- a/README.md +++ b/README.md @@ -50,7 +50,13 @@ Reconstruction attacks cover also attacks known as *model inversion* and *attrib - [**Privacy Risks of General-Purpose Language Models**](https://www.researchgate.net/profile/Xudong_Pan3/publication/340965355_Privacy_Risks_of_General-Purpose_Language_Models/links/5ea7ca55a6fdccd7945b6a7d/Privacy-Risks-of-General-Purpose-Language-Models.pdf) (Pan et al., 2020) - [**The secret revealer: generative model-inversion attacks against deep neural networks**](http://openaccess.thecvf.com/content_CVPR_2020/papers/Zhang_The_Secret_Revealer_Generative_Model-Inversion_Attacks_Against_Deep_Neural_Networks_CVPR_2020_paper.pdf)) (Zhang et al., 2020) - [**Inverting Gradients - How easy is it to break privacy in federated learning?**](https://arxiv.org/abs/2003.14053) (Geiping et al., 2020) - +- [**GAMIN: An Adversarial Approach to Black-Box Model Inversion**](https://arxiv.org/abs/1909.11835) (Aivodji et al., 2019) +- [**Adversarial Privacy Preservation under Attribute Inference Attack**](https://arxiv.org/abs/1906.07902) (Zhao et al., 2019) +- [**Reconstruction of training samples from loss functions**](https://arxiv.org/pdf/1805.07337.pdf) (Sannai, 2018) +- [**A Framework for Evaluating Gradient Leakage Attacks in Federated Learning**](https://arxiv.org/pdf/2004.10397.pdf) (Wei et al., 2020) +- [**Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning**](https://arxiv.org/pdf/1702.07464.pdf) (Hitaj et al., 2017) +- [**Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning**](https://arxiv.org/pdf/1812.00535.pdf) (Wang et al., 2018) +- [**Exploring Image Reconstruction Attack in Deep Learning Computation Offloading**](https://dl.acm.org/doi/pdf/10.1145/3325413.3329791) (Oh and Lee, 2019) ## Property inference - [**Hacking smart machines with smarter ones: How to extract meaningful data from machine learning classifiers**](https://dl.acm.org/doi/10.1504/IJSN.2015.071829) (Ateniese et al., 2015)