CalvinBackup/awesome-ml-privacy-attacks
1
0
Fork 0
mirror of https://github.com/stratosphereips/awesome-ml-privacy-attacks.git synced 2025-12-15 23:59:39 +01:00
Code Issues Packages Projects Releases Wiki Activity

Update README.md

Browse Source
This commit is contained in:
Suha Sabi Hussain
2020-09-28 17:23:31 -04:00
committed by GitHub
parent a472f31055
commit 92d3b70bff
1 changed files with 6 additions and 0 deletions
Download Patch File Download Diff File Expand all files Collapse all files
README.md
+6
View File
@@ -61,6 +61,8 @@ This repository contains a curated list of papers related to privacy attacks aga
- [**Privacy Analysis of Deep Learning in the Wild: Membership Inference Attacks against Transfer Learning**](https://arxiv.org/abs/2009.04872) (Zou et al., 2020)
- [**Quantifying Membership Inference Vulnerability via Generalization Gap and Other Model Metrics**](https://arxiv.org/abs/2009.05669) (Bentley et al., 2020)
- [**MACE: A Flexible Framework for Membership Privacy Estimation in Generative Models**](https://arxiv.org/abs/2009.05683) (Liu et al., 2020)
- [**On Primes, Log-Loss Scores and (No) Privacy**](https://arxiv.org/abs/2009.08559) (Aggarwal et al., 2020)
- [**MCMIA: Model Compression Against Membership Inference Attack in Deep Neural Networks**](https://arxiv.org/abs/2008.13578) (Wang et al., 2020)
## Reconstruction
Reconstruction attacks cover also attacks known as *model inversion* and *attribute inference*.
@@ -133,7 +135,11 @@ Reconstruction attacks cover also attacks known as *model inversion* and *attrib
- [**MetaSimulator: Simulating Unknown Target Models for Query-Efficient Black-box Attacks**](https://arxiv.org/abs/2009.00960) (Chen and Yong, 2020)
- [**Prediction Poisoning: Towards Defenses Against DNN Model Stealing Attacks**](https://arxiv.org/abs/1906.10908) (Orekondy et al., 2019)
- [**IReEn: Iterative Reverse-Engineering of Black-Box Functions via Neural Program Synthesis**](https://arxiv.org/abs/2006.10720) (Hajipour et al., 2020)
- [**ES Attack: Model Stealing against Deep Neural Networks without Data Hurdles**](https://arxiv.org/abs/2009.09560) (Yuan et al., 2020)
# Other
- [**Toward Robustness and Privacy in Federated Learning: Experimenting with Local and Central Differential Privacy**](https://arxiv.org/abs/2009.03561) (Naseri et al., 2020)
- [**Analyzing Information Leakage of Updates to Natural Language Models**](https://arxiv.org/abs/1912.07942) (Brockschmidt et al., 2020)
- [**Estimating g-Leakage via Machine Learning**](https://arxiv.org/abs/2005.04399) (Romanelli et al., 2020)
- [**Information Leakage in Embedding Models**](https://arxiv.org/abs/2004.00053) (Song and Raghunathan, 2020)
- [**Hide-and-Seek Privacy Challenge**](https://arxiv.org/abs/2007.12087) (Jordan et al., 2020)