From 1008096ff758fc91d1b7586daaa3050b7931a29f Mon Sep 17 00:00:00 2001
From: abigailgold <57357634+abigailgold@users.noreply.github.com>
Date: Mon, 5 Jul 2021 15:29:40 +0300
Subject: [PATCH] Updated Relevant papers (markdown)

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

diff --git a/Relevant-papers.md b/Relevant-papers.md
index 7700342..89d49f0 100644
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+++ b/Relevant-papers.md
@@ -37,6 +37,14 @@ Modelling and Quantifying Membership Information Leakage in Machine Learning (20
 
 ML Privacy Meter: Aiding Regulatory Compliance by Quantifying the Privacy Risks of Machine Learning (2020): https://arxiv.org/abs/2007.09339
 
+Quantifying Membership Inference Vulnerability via Generalization Gap and Other Model Metrics (2020): https://arxiv.org/abs/2009.05669
+
+Quantifying Membership Privacy via Information Leakage (2020): https://arxiv.org/abs/2010.05965
+
+Measuring Data Leakage in Machine-Learning Models with Fisher Information (2021): https://arxiv.org/abs/2102.11673
+
+Bounding Information Leakage in Machine Learning (2021): https://arxiv.org/pdf/2105.03875v1.pdf
+
 ## Differential privacy for ML models:
 Deep Learning with Differential Privacy (2016): https://arxiv.org/abs/1607.00133