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--- a/README.md
+++ b/README.md
@@ -1,6 +1,26 @@
 # Awesome-model-inversion-attack
 
-### Defending MIA on graphs
 
-2022 - DIFFERENTIALLY PRIVATE GRAPH CLASSIFICATION WITH GNNS
-[paper](https://arxiv.org/pdf/2202.02575.pdf)
+
+### Graph Learning
+
+WWW 2022 - Learning Privacy-Preserving Graph Convolutional Network with Partially Observed Sensitive Attributes.
+[[paper]](https://dl.acm.org/doi/pdf/10.1145/3485447.3511975?casa_token=Xsle4t9cLdcAAAAA:Gmij-qWaTJ2esGVa-yzKNHqVOMzYyaIgdNcgGmEzHrVyMdwwf9idn3qBjkhCcQeRTvbAkaT6OxiwXsk)
+
+Arxiv 2022 - DIFFERENTIALLY PRIVATE GRAPH CLASSIFICATION WITH GNNS.
+[[paper]](https://arxiv.org/pdf/2202.02575.pdf)
+
+Arxiv 2022 - GAP: Differentially Private Graph Neural Networks with Aggregation Perturbation.
+[[paper]](https://arxiv.org/pdf/2203.00949.pdf)
+
+Arxiv 2022 - SOK: DIFFERENTIAL PRIVACY ON GRAPH-STRUCTURED DATA.
+[[paper]](https://arxiv.org/pdf/2203.09205.pdf)
+
+Arxiv 2022 - Degree-Preserving Randomized Response for Graph Neural Networks under Local Differential Privacy.
+[[paper]](https://arxiv.org/pdf/2202.10209.pdf)
+
+Arxiv 2022 - Private Graph Extraction via Feature Explanations.
+[[paper]](https://arxiv.org/pdf/2206.14724.pdf)
+
+
+<!--   [[paper]]()   -->