From 759dffa7c6f5ad1426e0590f2d71af3e150d5f1c Mon Sep 17 00:00:00 2001 From: MariaRigaki Date: Thu, 16 Jul 2020 07:54:43 +0200 Subject: [PATCH] Added link to arxiv plus some minor fixes. --- README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 6d16629..54f758d 100644 --- a/README.md +++ b/README.md @@ -2,8 +2,8 @@ This repository contains a curated list of papers related to privacy attacks against machine learning. A code repository is provided when available by the authors. For corrections, suggestions or missing papers, please either open an issue or submit a pull request. # Surveys and Overviews +- [**A Survey of Privacy Attacks in Machine Learning**](https://arxiv.org/abs/2007.07646) (Rigaki and Garcia, 2020) - [**An Overview of Privacy in Machine Learning**](https://arxiv.org/pdf/2005.08679) (De Cristofaro, 2020) -- **A Survey of Privacy Attacks in Machine Learning** (Rigaki and Garcia, 2020) # Papers and Code @@ -42,7 +42,7 @@ Reconstruction attacks cover also attacks known as *model inversion* and *attrib - [**Neural network inversion in adversarial setting via background knowledge alignment**](https://dl.acm.org/doi/pdf/10.1145/3319535.3354261?casa_token=lDNQ40-4Wa4AAAAA:p9olQ3qMdDZ0n2sl-nNIgk4sOuLRMBTGVTxycZ5wjGpnFPf5lTz-MYw0e8ISggSseHC9T46it5yX) (Yang et al., 2019) - [**iDLG: Improved Deep Leakage from Gradients**](https://arxiv.org/pdf/2001.02610) (Zhao et al., 2020) ([code](https://github.com/PatrickZH/Improved-Deep-Leakage-from-Gradients)) - [**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)) ([link](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) +- [**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) @@ -58,7 +58,7 @@ Reconstruction attacks cover also attacks known as *model inversion* and *attrib - [**Copycat CNN: Stealing Knowledge by Persuading Confession with Random Non-Labeled Data**](https://ieeexplore.ieee.org/document/8489592) (Correia-Silva et al., 2018) ([code](https://github.com/jeiks/Stealing_DL_Models)) - [**Towards reverse-engineering black-box neural networks.**](https://openreview.net/forum?id=BydjJte0-)(Oh et al., 2018) ([code](https://github.com/coallaoh/WhitenBlackBox)) - [**Knockoff nets: Stealing functionality of black-box models**](http://openaccess.thecvf.com/content_CVPR_2019/papers/Orekondy_Knockoff_Nets_Stealing_Functionality_of_Black-Box_Models_CVPR_2019_paper.pdf) (Orekondy et al., 2019) ([code](https://github.com/tribhuvanesh/knockoffnets)) -- [**PRADA: protecting against DNN model stealing attacks**](https://ieeexplore.ieee.org/document/8806737) (juuti et al., 2019) ([code](https://github.com/SSGAalto/prada-protecting-against-dnn-model-stealing-attacks)) +- [**PRADA: protecting against DNN model stealing attacks**](https://ieeexplore.ieee.org/document/8806737) (Juuti et al., 2019) ([code](https://github.com/SSGAalto/prada-protecting-against-dnn-model-stealing-attacks)) - [**Model Reconstruction from Model Explanations**](https://dl.acm.org/doi/abs/10.1145/3287560.3287562) (Milli et al., 2019) - [**Exploring connections between active learning and model extraction**](https://www.usenix.org/system/files/sec20summer_chandrasekaran_prepub.pdf) (Chandrasekaran et al., 2020) - [**High Accuracy and High Fidelity Extraction of Neural Networks**](https://www.usenix.org/conference/usenixsecurity20/presentation/jagielski) (Jagielski et al., 2020)