From ab407c5d2eede5b41873362ba54b5bda558d1c0a Mon Sep 17 00:00:00 2001 From: Pritha Gupta Date: Wed, 21 Aug 2024 19:39:40 +0200 Subject: [PATCH] Update README.md --- README.md | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index e4a4080..83824ab 100644 --- a/README.md +++ b/README.md @@ -11,9 +11,8 @@ [![Paper](https://img.shields.io/badge/arXiv-2401.14283-red)](https://arxiv.org/abs/2401.14283) ### AutoML Approaches to Quantify and Detect Leakage -The AutoMLQuantILDetect package utilizes AutoML approaches to detect and quantify system information leakage. -It is an advanced toolkit that leverages the power of Automated Machine Learning (AutoML) to quantify information leakage accurately. -This package estimates mutual information (MI) within systems that release classification datasets. +The AutoMLQuantILDetect package utilizes AutoML approaches to accurately detect and quantify system information leakage. +We also provide different approaches to estimate mutual information (MI) within systems that release classification datasets to quantify system information leakage. By leveraging state-of-the-art statistical tests, it precisely quantifies mutual information (MI) and effectively detects information leakage within classification datasets. With AutoMLQuantILDetect, users can confidently and comprehensively address the critical challenges of quantification and detection in information leakage analysis.