From 4a4e536598324279bf83763ed6238c04be1e14ea Mon Sep 17 00:00:00 2001
From: Xiong PENG <106721409+AlanPeng0897@users.noreply.github.com>
Date: Sat, 15 Jul 2023 22:01:21 +0800
Subject: [PATCH] Update README.md

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 README.md | 4 ----
 1 file changed, 4 deletions(-)

diff --git a/README.md b/README.md
index 95e626b..81edbbe 100644
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@@ -14,7 +14,6 @@ If some related papers are missing, please contact us via pull requests :)
 - [What is the model inversion attack?](#what-is-the-model-inversion-attack)
 - [Survey](#survey)
 - [Computer vision domain](#computer-vision-domain)
-  - [TODO](#todo)
 - [Graph learning domain](#graph-learning-domain)
 - [Natural language processing domain](#natural-language-processing-domain)
 - [Tools](#tools)
@@ -31,9 +30,6 @@ 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.)
 
 ## Survey
-Arxiv 2021 - A Survey of Privacy Attacks in Machine Learning.
-[[paper]](https://arxiv.org/pdf/2007.07646.pdf)
-
 Arxiv 2022 - A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability.
 [[paper]](https://arxiv.org/pdf/2204.08570.pdf)