CalvinBackup/Awesome-model-inversion-attack
1
0
Fork 0
mirror of https://github.com/AndrewZhou924/Awesome-model-inversion-attack.git synced 2026-08-22 08:57:12 +02:00
Code Issues Packages Projects Releases Wiki Activity

update README

Browse Source
This commit is contained in:
AndrewZhou924
2023-06-18 13:47:24 +08:00
parent 363a54e364
commit 5daed97606
1 changed files with 30 additions and 8 deletions
Download Patch File Download Diff File Expand all files Collapse all files
README.md
+30 -8
View File
@@ -7,7 +7,11 @@
A curated list of resources for model inversion attack (MIA). A curated list of resources for model inversion attack (MIA).
If some related papers are missing, please contact us via pull requests. If some related papers are missing, please contact us via pull requests.
### What is the model inversion attack? **Outlines:**
[TOC]
## What is the model inversion attack?
A model inversion attack is a privacy attack where the attacker is able to reconstruct the original samples that were used to train the synthetic model from the generated synthetic data set. (Mostly.ai) A model inversion attack is a privacy attack where the attacker is able to reconstruct the original samples that were used to train the synthetic model from the generated synthetic data set. (Mostly.ai)
@@ -16,7 +20,7 @@ The goal of model inversion attacks is to recreate training data or sensitive at
In model inversion attacks, a malicious user attempts to recover the private dataset used to train a supervised neural network. A successful model inversion attack should generate realistic and diverse samples that accurately describe each of the classes in the private dataset. (Wang et al, 2021.) In model inversion attacks, a malicious user attempts to recover the private dataset used to train a supervised neural network. A successful model inversion attack should generate realistic and diverse samples that accurately describe each of the classes in the private dataset. (Wang et al, 2021.)
### Survey ## Survey
Arxiv 2021 - A Survey of Privacy Attacks in Machine Learning. Arxiv 2021 - A Survey of Privacy Attacks in Machine Learning.
[[paper]](https://arxiv.org/pdf/2007.07646.pdf) [[paper]](https://arxiv.org/pdf/2007.07646.pdf)
@@ -34,7 +38,7 @@ Philosophical Transactions of the Royal Society A 2018. Algorithms that remember
[[paper]](https://royalsocietypublishing.org/doi/pdf/10.1098/rsta.2018.0083) [[paper]](https://royalsocietypublishing.org/doi/pdf/10.1098/rsta.2018.0083)
### Computer vision domain ## Computer vision domain
USENIX Security 2014 - Privacy in Pharmacogenetics: An End-to-End Case Study of Personalized Warfarin Dosing. USENIX Security 2014 - Privacy in Pharmacogenetics: An End-to-End Case Study of Personalized Warfarin Dosing.
[[paper]](https://www.usenix.org/system/files/conference/usenixsecurity14/sec14-paper-fredrikson-privacy.pdf) [[paper]](https://www.usenix.org/system/files/conference/usenixsecurity14/sec14-paper-fredrikson-privacy.pdf)
@@ -46,6 +50,10 @@ CCS 2015 - Model Inversion Attacks that Exploit Confidence Information and Basic
[[code3]](https://github.com/zhangzp9970/MIA) [[code3]](https://github.com/zhangzp9970/MIA)
[[code4]](https://github.com/sarahsimionescu/simple-model-inversion) [[code4]](https://github.com/sarahsimionescu/simple-model-inversion)
IJCAI 2015 - Regression model fitting under differential privacy and model inversion attack.
[[paper]](http://www.csce.uark.edu/~xintaowu/publ/ijcai15.pdf)
[[code]](https://github.com/cxs040/Regression-Model-Fitting-under-Differential-Privacy-and-Model-Inversion-Attack-Source-Code)
CSF 2016 - A Methodology for Formalizing Model-Inversion Attacks. CSF 2016 - A Methodology for Formalizing Model-Inversion Attacks.
[[paper]](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7536387&casa_token=ClIVAMYo6dcAAAAA:u75HHyFHj5lBRec9h5SqOZyAsL2dICcWIuQPCj6ltk8McREFCaM4ex42mv3S-oNPiGJLDfUqg0qL) [[paper]](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7536387&casa_token=ClIVAMYo6dcAAAAA:u75HHyFHj5lBRec9h5SqOZyAsL2dICcWIuQPCj6ltk8McREFCaM4ex42mv3S-oNPiGJLDfUqg0qL)
@@ -59,6 +67,13 @@ PST 2017 - Model inversion attacks for prediction systems: Without knowledge of
CSF 2018 - Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting. CSF 2018 - Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting.
[[paper]](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8429311) [[paper]](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8429311)
GLSVLSI 2019 - MLPrivacyGuard: Defeating Confidence Information based Model Inversion Attacks on Machine Learning Systems.
[[paper]](https://www.researchgate.net/profile/Tiago-Alves-13/publication/333136362_MLPrivacyGuard_Defeating_Confidence_Information_based_Model_Inversion_Attacks_on_Machine_Learning_Systems/links/5cddb94d92851c4eaba682d7/MLPrivacyGuard-Defeating-Confidence-Information-based-Model-Inversion-Attacks-on-Machine-Learning-Systems.pdf)
ACSAC 2019 - Model inversion attacks against collaborative inference.
[[paper]](https://par.nsf.gov/servlets/purl/10208164)
[[code]](https://github.com/zechenghe/Inverse_Collaborative_Inference)
CCS 2019 - Neural Network Inversion in Adversarial Setting via Background Knowledge Alignment. CCS 2019 - Neural Network Inversion in Adversarial Setting via Background Knowledge Alignment.
[[paper]](https://dl.acm.org/doi/pdf/10.1145/3319535.3354261?casa_token=J81Ps-ZWXHkAAAAA:FYnXo7DQoHpdhqns8x2TclKFeHpAQlXVxMBW2hTrhJ5c20XKdsounqdT1Viw1g6Xsu9FtKj85elxQaA) [[paper]](https://dl.acm.org/doi/pdf/10.1145/3319535.3354261?casa_token=J81Ps-ZWXHkAAAAA:FYnXo7DQoHpdhqns8x2TclKFeHpAQlXVxMBW2hTrhJ5c20XKdsounqdT1Viw1g6Xsu9FtKj85elxQaA)
[[code]](https://github.com/zhangzp9970/TB-MIA) [[code]](https://github.com/zhangzp9970/TB-MIA)
@@ -87,9 +102,16 @@ APSIPA ASC 2020 - Deep Face Recognizer Privacy Attack: Model Inversion Initializ
USENIX Security 2020 - Updates-Leak: Data Set Inference and Reconstruction Attacks in Online Learning. USENIX Security 2020 - Updates-Leak: Data Set Inference and Reconstruction Attacks in Online Learning.
[[paper]](https://www.usenix.org/system/files/sec20-salem.pdf) [[paper]](https://www.usenix.org/system/files/sec20-salem.pdf)
IoTJ 2020 - Attacking and Protecting Data Privacy in Edge-Cloud Collaborative Inference Systems.
[[paper]](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9187880)
[[code]](https://github.com/zechenghe/Inverse_Collaborative_Inference)
ECCV 2020 Workshop - Black-Box Face Recovery from Identity Features. ECCV 2020 Workshop - Black-Box Face Recovery from Identity Features.
[[paper]](https://arxiv.org/pdf/2007.13635.pdf) [[paper]](https://arxiv.org/pdf/2007.13635.pdf)
Arxiv 2020 - Defending model inversion and membership inference attacks via prediction purification.
[[paper]](https://arxiv.org/pdf/2005.03915.pdf)
IJCAI 2021 - Contrastive Model Inversion for Data-Free Knowledge Distillation. IJCAI 2021 - Contrastive Model Inversion for Data-Free Knowledge Distillation.
[[paper]](https://www.ijcai.org/proceedings/2021/0327.pdf) [[paper]](https://www.ijcai.org/proceedings/2021/0327.pdf)
[[code]](https://github.com/zju-vipa/CMI) [[code]](https://github.com/zju-vipa/CMI)
@@ -187,7 +209,7 @@ Arxiv 2022 - Defending against Reconstruction Attacks through Differentially Pri
IEEE 2021 - Defending Against Model Inversion Attack by Adversarial Examples. IEEE 2021 - Defending Against Model Inversion Attack by Adversarial Examples.
[[paper]](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9527945&tag=1) [[paper]](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9527945&tag=1)
### Graph learning domain ## Graph learning domain
USENIX Security 2020 - Stealing Links from Graph Neural Networks. USENIX Security 2020 - Stealing Links from Graph Neural Networks.
[[paper]](https://www.usenix.org/system/files/sec21-he-xinlei.pdf) [[paper]](https://www.usenix.org/system/files/sec21-he-xinlei.pdf)
@@ -250,7 +272,7 @@ ICML 2023 - On Strengthening and Defending Graph Reconstruction Attack with Mark
[[paper]](https://openreview.net/pdf?id=Vcl3qckVyh) [[paper]](https://openreview.net/pdf?id=Vcl3qckVyh)
[[code]](https://github.com/tmlr-group/MC-GRA) [[code]](https://github.com/tmlr-group/MC-GRA)
### Natural language processing domain ## Natural language processing domain
CCS 2020 - Information Leakage in Embedding Models. CCS 2020 - Information Leakage in Embedding Models.
[[paper]](https://dl.acm.org/doi/pdf/10.1145/3372297.3417270?casa_token=0ltuTKcG5cIAAAAA:YcpnOm4WlV0UnSS2dOWdtcnFh6DqSygG9MuS31gGQEgMxOBHQKeXsoNGkFhEw8gvlqY78gTkaRn9gUo) [[paper]](https://dl.acm.org/doi/pdf/10.1145/3372297.3417270?casa_token=0ltuTKcG5cIAAAAA:YcpnOm4WlV0UnSS2dOWdtcnFh6DqSygG9MuS31gGQEgMxOBHQKeXsoNGkFhEw8gvlqY78gTkaRn9gUo)
@@ -262,7 +284,7 @@ Arxiv 2022 - Text Revealer: Private Text Reconstruction via Model Inversion Atta
[[paper]](https://arxiv.org/pdf/2209.10505.pdf) [[paper]](https://arxiv.org/pdf/2209.10505.pdf)
### Tools ## Tools
[AIJack](https://github.com/Koukyosyumei/AIJack): Implementation of algorithms for AI security. [AIJack](https://github.com/Koukyosyumei/AIJack): Implementation of algorithms for AI security.
[Privacy-Attacks-in-Machine-Learning](https://github.com/shrebox/Privacy-Attacks-in-Machine-Learning): Membership Inference, Attribute Inference and Model Inversion attacks implemented using PyTorch. [Privacy-Attacks-in-Machine-Learning](https://github.com/shrebox/Privacy-Attacks-in-Machine-Learning): Membership Inference, Attribute Inference and Model Inversion attacks implemented using PyTorch.
@@ -270,7 +292,7 @@ Arxiv 2022 - Text Revealer: Private Text Reconstruction via Model Inversion Atta
[ml-attack-framework](https://github.com/Pilladian/ml-attack-framework): Universität des Saarlandes - Privacy Enhancing Technologies 2021 - Semester Project. [ml-attack-framework](https://github.com/Pilladian/ml-attack-framework): Universität des Saarlandes - Privacy Enhancing Technologies 2021 - Semester Project.
### Others ## Others
2019 - Uncovering a model’s secrets. 2019 - Uncovering a model’s secrets.
[[blog1]](https://gab41.lab41.org/uncovering-a-models-secrets-model-inversion-part-i-ce460eab93d6) [[blog1]](https://gab41.lab41.org/uncovering-a-models-secrets-model-inversion-part-i-ce460eab93d6)
[[blog2]](https://gab41.lab41.org/robust-or-private-model-inversion-part-ii-94d54fd8d4a5) [[blog2]](https://gab41.lab41.org/robust-or-private-model-inversion-part-ii-94d54fd8d4a5)
@@ -285,7 +307,7 @@ Arxiv 2022 - Text Revealer: Private Text Reconstruction via Model Inversion Atta
[[slides]](https://www.cs.toronto.edu/~toni/Courses/Fairness/Lectures/ML-and-DP-v2.pdf) [[slides]](https://www.cs.toronto.edu/~toni/Courses/Fairness/Lectures/ML-and-DP-v2.pdf)
### Related repositories ## Related repositories
awesome-ml-privacy-attacks awesome-ml-privacy-attacks
[[repo]](https://github.com/stratosphereips/awesome-ml-privacy-attacks#reconstruction) [[repo]](https://github.com/stratosphereips/awesome-ml-privacy-attacks#reconstruction)