From b6edaed0fe9700ff1539742d9eb0160ee1669b26 Mon Sep 17 00:00:00 2001
From: Xiong PENG <106721409+AlanPeng0897@users.noreply.github.com>
Date: Sat, 15 Jul 2023 21:59:04 +0800
Subject: [PATCH] Update README.md

---
 README.md | 48 ++++++++++++++++++++++++------------------------
 1 file changed, 24 insertions(+), 24 deletions(-)

diff --git a/README.md b/README.md
index be549ee..95e626b 100644
--- a/README.md
+++ b/README.md
@@ -197,33 +197,33 @@ Philosophical Transactions of the Royal Society A 2018. Algorithms that remember
 | 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) |
 
 
+<!--
 ### TODO
 | Year          | Title                                                                                         | Adversarial Knowledge | Venue              | Paper Link                                                                                                                          | Code Link                                                   |
 | :-----------: | --------------------------------------------------------------------------------------------- | --------------------- | ------------------ | ---------------------------------------------------------------------------------------------------------------------------------- | ----------------------------------------------------------- |
-| 2019   | The secret sharer: Evaluating and testing unintended memorization in neural networks          | white-box             | USENIX         | [Paper](https://www.usenix.org/system/files/sec19-carlini.pdf)                                                                      |                                                             |
-| 2019     | Deep leakage from gradients                                                                   | white-box             | NIPS         | [Paper](https://papers.nips.cc/paper/9617-deep-leakage-from-gradients.pdf)                                                        | [code](https://github.com/mit-han-lab/dlg)                   |
-| 2020     | Inverting Gradients - How easy is it to break privacy in federated learning?                  | white-box             | NIPS             | [Paper](https://arxiv.org/abs/2003.14053)                                                                                          |                                                             |
-| 2018    | Reconstruction of training samples from loss functions                                        | -                     | arXiv        | [Paper](https://arxiv.org/pdf/1805.07337.pdf)                                                                                      |                                                             |
-| 2022     | Reconstructing Training Data from Trained Neural Networks                                      | white-box             | NIPS             | [Paper](https://arxiv.org/abs/2206.07758)                                                                                          |                                                             |
-| 2020     | A Framework for Evaluating Gradient Leakage Attacks in Federated Learning                     | white-box             | CoRR             | [Paper](https://arxiv.org/pdf/2004.10397.pdf)                                                                                      |                                                             |
-| 2017      | Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning                | both                  | CCS              | [Paper](https://arxiv.org/pdf/1702.07464.pdf)                                                                                      |                                                             |
-| 2019 | Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning | white-box             | IEEE INFOCOM     | [Paper](https://arxiv.org/pdf/1812.00535.pdf)                                                                                      |                                                             |
-| 2020    | Evaluation Indicator for Model Inversion Attack                                                | metric                | AdvML            | [Paper](https://drive.google.com/file/d/1rl77BGtGHzZ8obWUEOoqunXCjgvpzE8d/view)                                                     |                                                             |
-| -            | An Attack-Based Evaluation Method for Differentially Private Learning Against Model Inversion Attack | white-box             | arXiv            | [Paper](https://ieeexplore.ieee.org/document/8822435)                                                                              |                                                             |
-| 2020    | SAPAG: A Self-Adaptive Privacy Attack From Gradients                                           | white-box             | arXiv            | [Paper](https://arxiv.org/abs/2009.06228)                                                                                          |                                                             |
-| 2022   | Exploring the Security Boundary of Data Reconstruction via Neuron Exclusivity Analysis                | white-box             | USENIX                              | [Paper](https://arxiv.org/abs/2010.13356)                                                                                          |           |
-| 2020    | Knowledge-Enriched Distributional Model Inversion Attacks                                              | white-box             | arXiv                               | [Paper](https://arxiv.org/abs/2010.04092)                                                                                          |           |
-| 2020    | MixCon: Adjusting the Separability of Data Representations for Harder Data Recovery                   | white-box             | arXiv                               | [Paper](https://arxiv.org/abs/2010.11463)                                                                                          |           |
-| 2020    | Evaluation of Inference Attack Models for Deep Learning on Medical Data                                | black-box             | arXiv                               | [Paper](https://arxiv.org/abs/2011.00177)                                                                                          |           |
-| 2020    | FaceLeaks: Inference Attacks against Transfer Learning Models via Black-box Queries                   | black-box             | arXiv                               | [Paper](https://arxiv.org/abs/2010.14023)                                                                                          |           |
-| -             | Derivation of Constraints from Machine Learning Models and Applications to Security and Privacy        | Theory                | A                                     | [Paper](https://hal.archives-ouvertes.fr/hal-03091740/)                                                                            |           |
-| 2021 | On the (In)Feasibility of Attribute Inference Attacks on Machine Learning Models                     | both                  | IEEE EuroS&P                        | [Paper](https://arxiv.org/abs/2103.07101)                                                                                          |           |
-| 2021     | R-GAP: Recursive Gradient Attack on Privacy                                                           | white-box             | ICLR                                | [Paper](https://arxiv.org/abs/2010.07733)                                                                                          |           |
-| 2021    | PRECODE - A Generic Model Extension to Prevent Deep Gradient Leakage                                  | white-box             | arXiv                               | [Paper](https://arxiv.org/abs/2108.04725)                                                                                          |           |
-| 2021    | On the Importance of Encrypting Deep Features                                                          | black-box             | arXiv                               | [Paper](https://arxiv.org/abs/2108.07147)                                                                                          |           |
-| 2022    | Reconstructing Training Data with Informed Adversaries                                                  | white-box             | arXiv                               | [Paper](https://arxiv.org/abs/2201.04845)                                                                                          |           |
-| 2022    | Privacy Vulnerability of Split Computing to Data-Free Model Inversion Attacks                          | white-box             | arXiv                               | [Paper](https://arxiv.org/abs/2107.06304)                                                                                          |           |
-
+| xxx 2019   | The secret sharer: Evaluating and testing unintended memorization in neural networks          | white-box             | USENIX         | [Paper](https://www.usenix.org/system/files/sec19-carlini.pdf)                                                                      |                                                             |
+| xxx 2019     | Deep leakage from gradients                                                                   | white-box             | NIPS         | [Paper](https://papers.nips.cc/paper/9617-deep-leakage-from-gradients.pdf)                                                        | [code](https://github.com/mit-han-lab/dlg)                   |
+| xxx 2020     | Inverting Gradients - How easy is it to break privacy in federated learning?                  | white-box             | NIPS             | [Paper](https://arxiv.org/abs/2003.14053)                                                                                          |                                                             |
+| xxx 2018    | Reconstruction of training samples from loss functions                                        | -                     | arXiv        | [Paper](https://arxiv.org/pdf/1805.07337.pdf)                                                                                      |                                                             |
+| vvv 2022     | Reconstructing Training Data from Trained Neural Networks                                      | white-box             | NIPS             | [Paper](https://arxiv.org/abs/2206.07758)                                                                                          |                                                             |
+| xxx 2020     | A Framework for Evaluating Gradient Leakage Attacks in Federated Learning                     | white-box             | CoRR             | [Paper](https://arxiv.org/pdf/2004.10397.pdf)                                                                                      |                                                             |
+| xxx 2017      | Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning                | both                  | CCS              | [Paper](https://arxiv.org/pdf/1702.07464.pdf)                                                                                      |                                                             |
+| xxx 2019 | Beyond Inferring Class Representatives: User-Level Privacy Leakage From Federated Learning | white-box             | IEEE INFOCOM     | [Paper](https://arxiv.org/pdf/1812.00535.pdf)                                                                                      |                                                             |
+| vvv 2020    | Evaluation Indicator for Model Inversion Attack                                                | metric                | AdvML            | [Paper](https://drive.google.com/file/d/1rl77BGtGHzZ8obWUEOoqunXCjgvpzE8d/view)                                                     |                                                             |
+| vvv 2019   | An Attack-Based Evaluation Method for Differentially Private Learning Against Model Inversion Attack | white-box             | arXiv            | [Paper](https://ieeexplore.ieee.org/document/8822435)                                                                              |                                                             |
+| xxx 2020    | SAPAG: A Self-Adaptive Privacy Attack From Gradients                                           | white-box             | arXiv            | [Paper](https://arxiv.org/abs/2009.06228)                                                                                          |                                                             |
+| xxx 2022   | Exploring the Security Boundary of Data Reconstruction via Neuron Exclusivity Analysis                | white-box             | USENIX                              | [Paper](https://arxiv.org/abs/2010.13356)                                                                                          |           |
+| vvv 2020    | MixCon: Adjusting the Separability of Data Representations for Harder Data Recovery                   | white-box             | arXiv                               | [Paper](https://arxiv.org/abs/2010.11463)                                                                                          |           |
+| xxx 2020    | Evaluation of Inference Attack Models for Deep Learning on Medical Data                                | black-box             | arXiv                               | [Paper](https://arxiv.org/abs/2011.00177)                                                                                          |           |
+| xxx 2020    | FaceLeaks: Inference Attacks against Transfer Learning Models via Black-box Queries                   | black-box             | arXiv                               | [Paper](https://arxiv.org/abs/2010.14023)                                                                                          |           |
+| xxx    | Derivation of Constraints from Machine Learning Models and Applications to Security and Privacy        | Theory                | A                                     | [Paper](https://hal.archives-ouvertes.fr/hal-03091740/)                                                                            |           |
+| xxx 2021 | On the (In)Feasibility of Attribute Inference Attacks on Machine Learning Models                     | both                  | IEEE EuroS&P                        | [Paper](https://arxiv.org/abs/2103.07101)                                                                                          |           |
+| xxx 2021     | R-GAP: Recursive Gradient Attack on Privacy                                                           | white-box             | ICLR                                | [Paper](https://arxiv.org/abs/2010.07733)                                                                                          |           |
+| xxx 2021    | PRECODE - A Generic Model Extension to Prevent Deep Gradient Leakage                                  | white-box             | arXiv                               | [Paper](https://arxiv.org/abs/2108.04725)                                                                                          |           |
+| xxx 2021    | On the Importance of Encrypting Deep Features                                                          | black-box             | arXiv                               | [Paper](https://arxiv.org/abs/2108.07147)                                                                                          |           |
+| vvv 2022    | Reconstructing Training Data with Informed Adversaries                                                  | white-box             | SP                               | [Paper](https://arxiv.org/abs/2201.04845)                                                                                          |           |
+| vvv 2022    | Privacy Vulnerability of Split Computing to Data-Free Model Inversion Attacks                          | white-box             | BMVC                               | [Paper](https://arxiv.org/abs/2107.06304)                                                                                          |           |
+-->
 
 <!-- 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)