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eziaowonder
@@ -39,7 +39,7 @@ Philosophical Transactions of the Royal Society A 2018. Algorithms that remember
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## Computer vision domain
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| Year | Title | Adversarial Knowledge | Venue | Paper Link | Code Link |
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<!-- | Year | Title | Adversarial Knowledge | Venue | Paper Link | Code Link |
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| ---- | ----- | -------------------- | ----- | ---------- | --------- |
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| 2014 | Privacy in pharmacogenetics: An end-to-end case study of personalized warfarin dosing | white-box | Security | [Paper](https://www.usenix.org/system/files/conference/usenixsecurity14/sec14-paper-fredrikson-privacy.pdf) | |
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| 2015 | Model inversion attacks that exploit confidence information and basic countermeasures | | CCS | [Paper](https://dl.acm.org/doi/pdf/10.1145/2810103.2813677) | [Code1](http://www.cs.cmu.edu/~mfredrik/mi-2016.zip), [Code2](https://github.com/yashkant/Model-Inversion-Attack), [Code3](https://github.com/zhangzp9970/MIA), [Code4](https://github.com/sarahsimionescu/simple-model-inversion) |
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@@ -97,9 +97,76 @@ Philosophical Transactions of the Royal Society A 2018. Algorithms that remember
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| 2023 | C2FMI: Corse-to-Fine Black-box Model Inversion Attack | black-box | TDSC | [Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10148574) | |
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| 2023 | Boosting Model Inversion Attacks with Adversarial Examples | black-box | TDSC | [Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10148576) | |
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| 2023 | Reinforcement Learning-Based Black-Box Model Inversion Attacks | black-box | CVPR | [Paper](https://arxiv.org/pdf/2304.04625.pdf) | [code](https://github.com/HanGyojin/RLB-MI) |
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| 2023 | Re-thinking Model Inversion Attacks Against Deep Neural Networks | white-box | CVPR | [Paper](https://arxiv.org/pdf/2304.01669.pdf) | [code](https://github.com/sutd-visual-computing-group/Re-thinking_MI) | -->
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| Year | Title | Adversarial Knowledge | Venue | Paper Link | Code Link |
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| ---- | ------------------------------------------------------------ | --------------------- | --------------- | ------------------------------------------------------------ | ------------------------------------------------------------ |
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| 2014 | Privacy in Pharmacogenetics: An End-to-End Case Study of Personalized Warfarin Dosing | white-box | USENIX Security | [paper](https://www.usenix.org/system/files/conference/usenixsecurity14/sec14-paper-fredrikson-privacy.pdf) | |
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| 2015 | Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures | white-box | CCS | [paper](https://dl.acm.org/doi/pdf/10.1145/2810103.2813677) | [code1](http://www.cs.cmu.edu/~mfredrik/mi-2016.zip), [code2](https://github.com/yashkant/Model-Inversion-Attack), [code3](https://github.com/zhangzp9970/MIA), [code4](https://github.com/sarahsimionescu/simple-model-inversion) |
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| 2015 | Regression model fitting under differential privacy and model inversion attack | white-box | IJCAI | [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) |
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| 2016 | A Methodology for Formalizing Model-Inversion Attacks | white-box | CSF | [paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7536387&casa_token=ClIVAMYo6dcAAAAA:u75HHyFHj5lBRec9h5SqOZyAsL2dICcWIuQPCj6ltk8McREFCaM4ex42mv3S-oNPiGJLDfUqg0qL) | |
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| 2017 | Machine Learning Models that Remember Too Much | white-box | CCS | [paper](https://arxiv.org/pdf/1709.07886.pdf) | [code](https://github.com/csong27/ml-model-remember) |
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| 2017 | Model inversion attacks for prediction systems: Without knowledge of non-sensitive attributes | white-box | PST | [paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8476925) | |
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| 2018 | Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting | white-box | CSF | [paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8429311) | |
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| 2019 | MLPrivacyGuard: Defeating Confidence Information based Model Inversion Attacks on Machine Learning Systems | white-box | GLSVLSI | [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) | |
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| 2019 | Model inversion attacks against collaborative inference | white-box | | | |
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| 2019 | Neural Network Inversion in Adversarial Setting via Background Knowledge Alignment | - | CCS | [Paper](https://dl.acm.org/doi/pdf/10.1145/3319535.3354261?casa_token=J81Ps-ZWXHkAAAAA:FYnXo7DQoHpdhqns8x2TclKFeHpAQlXVxMBW2hTrhJ5c20XKdsounqdT1Viw1g6Xsu9FtKj85elxQaA) | [Code](https://github.com/zhangzp9970/TB-MIA) | - |
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| 2019 | Exploiting Unintended Feature Leakage in Collaborative Learning | - | IEEE S&P | [Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8835269) | [Code](https://github.com/csong27/property-inference-collaborative-ml) | - |
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| 2019 | Adversarial Neural Network Inversion via Auxiliary Knowledge Alignment | - | Arxiv | [Paper](https://arxiv.org/pdf/1902.08552.pdf) | - | - |
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| 2019 | GAMIN: An Adversarial Approach to Black-Box Model Inversion | - | Arxiv | [Paper](https://arxiv.org/pdf/1909.11835.pdf) | - | - |
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| 2020 | The Secret Revealer: Generative Model-Inversion Attacks Against Deep Neural Networks | - | CVPR | [Paper](https://openaccess.thecvf.com/content_CVPR_2020/papers/Zhang_The_Secret_Revealer_Generative_Model-Inversion_Attacks_Against_Deep_Neural_Networks_CVPR_2020_paper.pdf) | [Code](https://github.com/AI-secure/GMI-Attack) | [Video](https://www.youtube.com/watch?v=_g-oXYMhz4M) |
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| 2020 | OVERLEARNING REVEALS SENSITIVE ATTRIBUTES | - | ICLR | [Paper](https://arxiv.org/pdf/1905.11742.pdf) | - | - |
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| 2020 | Deep Face Recognizer Privacy Attack: Model Inversion Initialization by a Deep Generative Adversarial Data Space Discriminator | - | APSIPA ASC | [Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9306253&casa_token=AWugOvIe0I0AAAAA:9wICCkMcfoljMqooM-lgl8m-6F6-cEl-ClHgNkE1SV8mZwqvBIaJ1HDjT1RWLyBz_P7tdB51jQVL&tag=1) | - | - |
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| 2020 | Updates-Leak: Data Set Inference and Reconstruction Attacks in Online Learning | - | USENIX Security | [Paper](https://www.usenix.org/system/files/sec20-salem.pdf) | - | - |
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| 2020 | Attacking and Protecting Data Privacy in Edge-Cloud Collaborative Inference Systems | - | IoTJ | [Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9187880) | [Code](https://github.com/zechenghe/Inverse_Collaborative_Inference) | - |
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| 2020 | Black-Box Face Recovery from Identity Features | - | ECCV Workshop | [Paper](https://arxiv.org/pdf/2007.13635.pdf) | - | - |
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| 2020 | Defending model inversion and membership inference attacks via prediction purification | - | Arxiv | [Paper](https://arxiv.org/pdf/2005.03915.pdf) | - | - |
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| 2020 | Generative model-inversion attacks against deep neural networks | - | CVPR | [Paper](https://arxiv.org/pdf/1911.07135.pdf) | [Code](https://github.com/AI-secure/GMI-Attack) | - |
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| 2020 | Privacy Preserving Facial Recognition Against Model Inversion Attacks | - | Globecom | [Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9322508) | - | - |
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| 2020 | Broadening Differential Privacy for Deep Learning Against Model Inversion Attacks | - | Big Data | [Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9378274) | - | - |
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| 2021 | Contrastive Model Inversion for Data-Free Knowledge Distillation | - | IJCAI | [Paper](https://www.ijcai.org/proceedings/2021/0327.pdf) | [Code](https://github.com/zju-vipa/CMI) | - |
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| 2021 | Membership Leakage in Label-Only Exposures | - | CCS | [Paper](https://yangzhangalmo.github.io/papers/CCS21-Label.pdf) | [Code](https://github.com/zhenglisec/decision-based-mia) | - |
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| 2021 | Black-box adversarial attacks on commercial speech platforms with minimal information | - | CCS | [Paper](https://dl.acm.org/doi/pdf/10.1145/3460120.3485383) | - | - |
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| 2021 | Unleashing the tiger: Inference attacks on split learning | - | CCS | [Paper](https://dl.acm.org/doi/pdf/10.1145/3460120.3485259) | [Code](https://github.com/pasquini-dario/SplitNN_FSHA) | - |
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| 2021 | See through gradients: Image batch recovery via gradinversion | - | CVPR | [Paper](http://openaccess.thecvf.com/content/CVPR2021/papers/Yin_See_Through_Gradients_Image_Batch_Recovery_via_GradInversion_CVPR_2021_paper.pdf) | - | - |
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| 2021 | Soteria: Provable defense against privacy leakage in federated learning from representation perspective | - | CVPR | [Paper](https://openaccess.thecvf.com/content/CVPR2021/papers/Sun_Soteria_Provable_Defense_Against_Privacy_Leakage_in_Federated_Learning_From_CVPR_2021_paper.pdf) | [Code](https://github.com/jeremy313/Soteria) | - |
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| 2021 | Imagine: Image synthesis by image-guided model inversion | - | CVPR | [Paper](https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_IMAGINE_Image_Synthesis_by_Image-Guided_Model_Inversion_CVPR_2021_paper.pdf) | - | - |
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| 2021 | Variational Model Inversion Attacks | - | NeurIPS | [Paper](https://proceedings.neurips.cc/paper/2021/file/50a074e6a8da4662ae0a29edde722179-Paper.pdf) | [Code](https://github.com/wangkua1/vmi) | - |
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| 2021 | Exploiting Explanations for Model Inversion Attacks | - | ICCV | [Paper](https://openaccess.thecvf.com/content/ICCV2021/papers/Zhao_Exploiting_Explanations_for_Model_Inversion_Attacks_ICCV_2021_paper.pdf) | - |
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| 2021 | Knowledge-Enriched Distributional Model Inversion Attacks | - | ICCV | [Paper](https://arxiv.org/pdf/2010.04092.pdf) | [Code](https://github.com/SCccc21/Knowledge-Enriched-DMI) |
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| 2021 | Improving Robustness to Model Inversion Attacks via Mutual Information Regularization | - | AAAI | [Paper](https://arxiv.org/pdf/2009.05241.pdf) | - |
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| 2021 | Label-Only Membership Inference Attack | - | ICML | [Paper](http://proceedings.mlr.press/v139/choquette-choo21a/choquette-choo21a.pdf) | [Code](https://github.com/cchoquette/membership-inference) |
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| 2021 | When Does Data Augmentation Help With Membership Inference Attacks? | - | ICML | [Paper](http://proceedings.mlr.press/v139/kaya21a/kaya21a.pdf) | - |
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| 2021 | PRACTICAL DEFENCES AGAINST MODEL INVERSION ATTACKS FOR SPLIT NEURAL NETWORKS | - | ICLR workshop | [Paper](https://arxiv.org/pdf/2104.05743.pdf) | [Code](https://github.com/TTitcombe/Model-Inversion-SplitNN) |
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| 2021 | Feature inference attack on model predictions in vertical federated learning | - | ICDE | [Paper](https://arxiv.org/pdf/2010.10152) | [Code](https://github.com/xj231/featureinference-vfl) |
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| 2021 | PRID: Model Inversion Privacy Attacks in Hyperdimensional Learning Systems | - | DAC | [Paper](https://dl.acm.org/doi/abs/10.1109/DAC18074.2021.9586217) | - |
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| 2021 | Robustness of on-device models: Adversarial attack to deep learning models on android apps | - | ICSE | [Paper](https://arxiv.org/pdf/2101.04401) | - |
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| 2021 | Defending Against Model Inversion Attack by Adversarial Examples | - | CSR Workshops | [Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9527945) | - |
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| 2021 | Practical Black Box Model Inversion Attacks Against Neural Nets | - | ECML PKDD | [Paper](https://link.springer.com/content/pdf/10.1007/978-3-030-93733-1.pdf?pdf=button) | - |
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| 2021 | Model Inversion Attack against a Face Recognition System in a Black-Box Setting | - | APSIPA | [Paper](http://www.apsipa.org/proceedings/2021/pdfs/0001800.pdf) | - |
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| 2022 | Plug & Play Attacks: Towards Robust and Flexible Model Inversion Attacks | - | ICML | [Paper](https://arxiv.org/pdf/2201.12179.pdf) | [Code](https://github.com/LukasStruppek/Plug-and-Play-Attacks) |
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| 2022 | Label-Only Model Inversion Attacks via Boundary Repulsion | - | CVPR | [Paper](https://arxiv.org/pdf/2203.01925.pdf) | [Code](https://github.com/m-kahla/Label-Only-Model-Inversion-Attacks-via-Boundary-Repulsion) |
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| 2022 | ResSFL: A Resistance Transfer Framework for Defending Model Inversion Attack in Split Federated Learning | - | CVPR | [Paper](https://openaccess.thecvf.com/content/CVPR2022/html/Li_ResSFL_A_Resistance_Transfer_Framework_for_Defending_Model_Inversion_Attack_CVPR_2022_paper.html) | [Code](https://github.com/zlijingtao/ResSFL) |
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| 2022 | Bilateral Dependency Optimization: Defending Against Model-inversion Attacks | - | KDD | [Paper](https://arxiv.org/pdf/2206.05483.pdf) | [Code](https://github.com/xpeng9719/Defend_MI) |
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| 2022 | ML-DOCTOR: Holistic Risk Assessment of Inference Attacks Against Machine Learning Models | - | USENIX Security | [Paper](https://www.usenix.org/system/files/sec22summer_liu-yugeng.pdf) | [Code](https://github.com/liuyugeng/ML-Doctor) |
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| 2022 | An Approximate Memory based Defense against Model Inversion Attacks to Neural Networks | - | IEEE | [Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9792582&casa_token=ymT57RhNhGEAAAAA:WsQHMpv77-4uyIp7l-p4hc7_Qmxvn5TeNpS5F7LHBFHyLay2O8Pe5eWqsKN2fu56v98NZsRrqeit) | [Code](https://github.com/katekemu/model_inversion_defense) |
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| 2022 | Model Inversion Attack by Integration of Deep Generative Models: Privacy-Sensitive Face Generation From a Face Recognition System | - | TIFS | [Paper](https://dl.acm.org/doi/abs/10.1109/TIFS.2022.3140687) | - |
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| 2022 | Defending against Reconstruction Attacks through Differentially Private Federated Learning for Classification of Heterogeneous Chest X-Ray Data | - | Arxiv | [Paper](https://arxiv.org/pdf/2205.03168.pdf) | - |
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| 2022 | One Parameter Defense—Defending Against Data Inference Attacks via Differential Privacy | black-box | TIFS | [Paper](https://arxiv.org/pdf/2203.06580.pdf) | |
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| 2022 | Reconstructing Training Data from Diverse ML Models by Ensemble Inversion | white-box | WACV | [Paper](https://arxiv.org/pdf/2111.03702.pdf) | |
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| 2022 | SecretGen: Privacy Recovery on Pre-trained Models via Distribution Discrimination | white-box | ECCV | [Paper](https://arxiv.org/pdf/2207.12263.pdf) | |
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| 2022 | UnSplit: Data-Oblivious Model Inversion, Model Stealing, andLabel Inference Attacks Against Split Learning | - | WPES | [Paper](https://arxiv.org/pdf/2108.09033.pdf) | [code](https://github.com/ege-erdogan/unsplit) |
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| 2022 | MIRROR: Model Inversion for Deep LearningNetwork with High Fidelity | white-box | NDSS | [Paper](https://www.cs.purdue.edu/homes/an93/static/papers/ndss2022_model_inversion.pdf) | [code](https://github.com/njuaplusplus/mirror) |
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| 2023 | Sparse Black-Box Inversion Attack with Limited Information | black-box | ICASSP | [Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10095514) | [code](https://github.com/Tencent/TFace/tree/master/recognition) |
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| 2023 | Breaching FedMD: Image Recovery via Paired-Logits Inversion Attack | black-box | CVPR | [Paper](https://arxiv.org/pdf/2304.11436.pdf) | [code](https://github.com/FLAIR-THU/PairedLogitsInversion) |
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| 2023 | Pseudo Label-Guided Model Inversion Attack via Conditional Generative Adversarial Network | white-box | AAAI | [Paper](https://arxiv.org/pdf/2302.09814.pdf) | [code](https://github.com/lethesec/plg-mi-attack) |
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| 2023 | C2FMI: Corse-to-Fine Black-box Model Inversion Attack | black-box | TDSC | [Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10148574) | |
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| 2023 | Boosting Model Inversion Attacks with Adversarial Examples | black-box | TDSC | [Paper](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10148576) | |
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| 2023 | Reinforcement Learning-Based Black-Box Model Inversion Attacks | black-box | CVPR | [Paper](https://arxiv.org/pdf/2304.04625.pdf) | [code](https://github.com/HanGyojin/RLB-MI) |
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| 2023 | Re-thinking Model Inversion Attacks Against Deep Neural Networks | white-box | CVPR | [Paper](https://arxiv.org/pdf/2304.01669.pdf) | [code](https://github.com/sutd-visual-computing-group/Re-thinking_MI) |
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USENIX Security 2014 - Privacy in Pharmacogenetics: An End-to-End Case Study of Personalized Warfarin Dosing.
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<!-- USENIX Security 2014 - Privacy in Pharmacogenetics: An End-to-End Case Study of Personalized Warfarin Dosing.
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[[paper]](https://www.usenix.org/system/files/conference/usenixsecurity14/sec14-paper-fredrikson-privacy.pdf)
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CCS 2015 - Model Inversion Attacks that Exploit Confidence Information and Basic Countermeasures.
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@@ -224,8 +291,8 @@ ICML 2021 - Label-Only Membership Inference Attack.
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[[paper]](http://proceedings.mlr.press/v139/choquette-choo21a/choquette-choo21a.pdf)
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[[code]](https://github.com/cchoquette/membership-inference)
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ICML 2021 - When Does Data Augmentation Help With Membership Inference Attacks?
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[[paper]](When Does Data Augmentation Help With Membership Inference Attacks?)
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ICML 2021 - When Does Data Augmentation Help With Membership Inference Attacks?
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[[paper]](http://proceedings.mlr.press/v139/kaya21a/kaya21a.pdf)
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ICLR 2021 workshop - PRACTICAL DEFENCES AGAINST MODEL INVERSION ATTACKS FOR SPLIT NEURAL NETWORKS.
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[[paper]](https://arxiv.org/pdf/2104.05743.pdf)
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@@ -322,18 +389,36 @@ CVPR 2023 - Reinforcement Learning-Based Black-Box Model Inversion Attacks
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CVPR 2023 - Re-thinking Model Inversion Attacks Against Deep Neural Networks
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[[paper]](https://arxiv.org/pdf/2304.01669.pdf)
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[[code]](https://github.com/sutd-visual-computing-group/Re-thinking_MI) |
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[[code]](https://github.com/sutd-visual-computing-group/Re-thinking_MI) | -->
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## Graph learning domain
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| Year | Title | Adversarial Knowledge | Venue | Paper Link | Code Link |
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| ---- | ----- | -------------------- | ----- | ---------- | --------- |
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| 2020 | Stealing Links from Graph Neural Networks | - | USENIX Security | [Paper](https://www.usenix.org/system/files/sec21-he-xinlei.pdf) | [Code](https://github.com/xinleihe/link_stealing_attack) |
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| 2020 | Improving Robustness to Model Inversion Attacks via Mutual Information Regularization | black & white-box | AAAI | [Paper](https://arxiv.org/pdf/2009.05241v1.pdf) | |
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| 2020 | Reducing Risk of Model Inversion Using Privacy-Guided Training | black & white-box | Arxiv | [Paper](https://arxiv.org/pdf/2006.15877.pdf) | |
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| 2020 | Quantifying Privacy Leakage in Graph Embedding | - | MobiQuitous | [Paper](https://arxiv.org/pdf/2010.00906.pdf) | [Code](https://github.com/vasishtduddu/GraphLeaks) |
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| 2021 | A Survey on Gradient Inversion: Attacks, Defenses and Future Directions | white-box | IJCAI | [Paper](https://arxiv.org/pdf/2206.07284.pdf) | |
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| 2021 | GraphMI: Extracting Private Graph Data from Graph Neural Networks | white-box | IJCAI | [Paper](https://arxiv.org/pdf/2106.02820v1.pdf) | [code](https://github.com/zaixizhang/GraphMI) |
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| 2021 | NetFense: Adversarial Defenses against Privacy Attacks on Neural Networks for Graph Data | black-box | ICDE | [Paper](https://arxiv.org/pdf/2106.11865.pdf) | [code](https://github.com/ICHproject/NetFense) |
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| 2021 | DeepWalking Backwards: From Node Embeddings Back to Graphs | - | ICML | [Paper](http://proceedings.mlr.press/v139/chanpuriya21a/chanpuriya21a.pdf) | [Code](https://github.com/konsotirop/Invert_Embeddings) |
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| 2021 | GraphMI: Extracting Private Graph Data from Graph Neural Networks | white-box | IJCAI | [Paper](https://arxiv.org/pdf/2106.02820v1.pdf) | [code](https://github.com/zaixizhang/GraphMI) |
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| 2021 | Node-Level Membership Inference Attacks Against Graph Neural Networks | - | Arxiv | [Paper](https://arxiv.org/pdf/2102.05429.pdf) | - |
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| 2022 | A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability | black & white-box | Arxiv | [Paper](https://arxiv.org/pdf/2204.08570.pdf) | |
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| 2022 | Learning Privacy-Preserving Graph Convolutional Network with Partially Observed Sensitive Attributes | - | WWW | [Paper](https://dl.acm.org/doi/pdf/10.1145/3485447.3511975?casa_token=Xsle4t9cLdcAAAAA:Gmij-qWaTJ2esGVa-yzKNHqVOMzYyaIgdNcgGmEzHrVyMdwwf9idn3qBjkhCcQeRTvbAkaT6OxiwXsk) | - |
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| 2022 | Inference Attacks Against Graph Neural Networks | - | USENIX Security | [Paper](https://www.usenix.org/system/files/sec22summer_zhang-zhikun.pdf) | [Code](https://github.com/Zhangzhk0819/GNN-Embedding-Leaks) |
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| 2022 | Model Stealing Attacks Against Inductive Graph Neural Networks | - | IEEE S&P | [Paper](https://arxiv.org/pdf/2112.08331.pdf) | [Code](https://github.com/xinleihe/GNNStealing) |
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| 2022 | DIFFERENTIALLY PRIVATE GRAPH CLASSIFICATION WITH GNNS | - | Arxiv | [Paper](https://arxiv.org/pdf/2202.02575.pdf) | - |
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| 2022 | GAP: Differentially Private Graph Neural Networks with Aggregation Perturbation | - | Arxiv | [Paper](https://arxiv.org/pdf/2203.00949.pdf) | - |
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| 2022 | SOK: DIFFERENTIAL PRIVACY ON GRAPH-STRUCTURED DATA | - | Arxiv | [Paper](https://arxiv.org/pdf/2203.09205.pdf) | - |
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| 2022 | Degree-Preserving Randomized Response for Graph Neural Networks under Local Differential Privacy | - | Arxiv | [Paper](https://arxiv.org/pdf/2202.10209.pdf) | - |
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| 2022 | Private Graph Extraction via Feature Explanations | - | Arxiv | [Paper](https://arxiv.org/pdf/2206.14724.pdf) | - |
|
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| 2022 | Privacy and Transparency in Graph Machine Learning: A Unified Perspective | - | Arxiv | [Paper](https://arxiv.org/pdf/2207.10896.pdf) | - |
|
||||
| 2022 | Finding MNEMON: Reviving Memories of Node Embeddings | - | CCS | [Paper](https://arxiv.org/pdf/2204.06963.pdf) | - |
|
||||
| 2022 | Defense against membership inference attack in graph neural networks through graph perturbation | - | IJIS | [Paper](https://link.springer.com/article/10.1007/s10207-022-00646-y) | - |
|
||||
| 2022 | Model Inversion Attacks against Graph Neural Networks | - | TKDE | [Paper](https://arxiv.org/pdf/2209.07807.pdf) | - |
|
||||
| 2023 | On Strengthening and Defending Graph Reconstruction Attack with Markov Chain Approximation | - | ICML | [Paper](https://openreview.net/pdf?id=Vcl3qckVyh) | [Code](https://github.com/tmlr-group/MC-GRA) |
|
||||
|
||||
<!--
|
||||
USENIX Security 2020 - Stealing Links from Graph Neural Networks.
|
||||
[[paper]](https://www.usenix.org/system/files/sec21-he-xinlei.pdf)
|
||||
[[code]](https://github.com/xinleihe/link_stealing_attack)
|
||||
@@ -393,7 +478,7 @@ TKDE 2022 - Model Inversion Attacks against Graph Neural Networks.
|
||||
|
||||
ICML 2023 - On Strengthening and Defending Graph Reconstruction Attack with Markov Chain Approximation.
|
||||
[[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
|
||||
| Year | Title | Adversarial Knowledge | Venue | Paper Link | Code Link |
|
||||
@@ -409,14 +494,14 @@ ICML 2023 - On Strengthening and Defending Graph Reconstruction Attack with Mark
|
||||
| 2022 | Recovering Private Text in Federated Learning of Language Models | white-box | NeurIPS | [paper](https://arxiv.org/pdf/2205.08514.pdf) | [code](https://github.com/princeton-sysml/film) |
|
||||
| 2023 | Sentence Embedding Leaks More Information than You Expect: Generative Embedding Inversion Attack to Recover the Whole Sentence | box | ACL | [paper](https://arxiv.org/pdf/2305.03010.pdf) | [code](https://github.com/hkust-knowcomp/geia) |
|
||||
|
||||
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)
|
||||
|
||||
USENIX Security 2021 - Extracting training data from large language models.
|
||||
[[paper]](https://www.usenix.org/system/files/sec21-carlini-extracting.pdf)
|
||||
|
||||
Arxiv 2022 - Text Revealer: Private Text Reconstruction via Model Inversion Attacks against Transformers.
|
||||
[[paper]](https://arxiv.org/pdf/2209.10505.pdf)
|
||||
[[paper]](https://arxiv.org/pdf/2209.10505.pdf) -->
|
||||
|
||||
|
||||
## Tools
|
||||
|
||||
Reference in New Issue
Block a user