From 47c2b895043c56f07cc4f34aae12032f58b89a85 Mon Sep 17 00:00:00 2001
From: abigailgold <57357634+abigailgold@users.noreply.github.com>
Date: Mon, 14 Jun 2021 15:50:00 +0300
Subject: [PATCH] Updated Relevant papers (markdown)

---
 Relevant-papers.md | 8 ++++----
 1 file changed, 4 insertions(+), 4 deletions(-)

diff --git a/Relevant-papers.md b/Relevant-papers.md
index 3f49f9d..0de36cf 100644
--- a/Relevant-papers.md
+++ b/Relevant-papers.md
@@ -21,16 +21,16 @@ Model Inversion Attacks that Exploit Confidence Information and Basic Countermea
 
 On the (In)Feasibility of Attribute Inference Attacks on Machine Learning Models (2021): https://arxiv.org/abs/2103.07101
 
-### Additional privacy attacks/metrics:
+### Additional privacy attacks:
 Updates-Leak: Data Set Inference and Reconstruction Attacks in Online Learning (2019): https://arxiv.org/pdf/1904.01067.pdf
 
+## Risk assessment of ML models:
 Towards Measuring Membership Privacy (2017): https://arxiv.org/abs/1712.09136
 
-Modelling and Quantifying Membership Information Leakage in Machine Learning (2020): https://ui.adsabs.harvard.edu/abs/2020arXiv200110648F/abstract
-
-## Risk assessment of ML models:
 Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting (2018): https://www.cs.cmu.edu/~mfredrik/papers/YeomCSF18.pdf
 
+Modelling and Quantifying Membership Information Leakage in Machine Learning (2020): https://ui.adsabs.harvard.edu/abs/2020arXiv200110648F/abstract
+
 ML Privacy Meter: Aiding Regulatory Compliance by Quantifying the Privacy Risks of Machine Learning (2020): https://arxiv.org/abs/2007.09339
 
 ## Differential privacy for ML models: