From e6603cde4b5628f9d35e9eb3d8d10822f90d3a20 Mon Sep 17 00:00:00 2001
From: fria <138676274+friadev@users.noreply.github.com>
Date: Sat, 12 Jul 2025 07:30:06 -0500
Subject: [PATCH] add more detail

---
 blog/posts/differential-privacy.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/blog/posts/differential-privacy.md b/blog/posts/differential-privacy.md
index d1ad3c35..96839902 100644
--- a/blog/posts/differential-privacy.md
+++ b/blog/posts/differential-privacy.md
@@ -107,7 +107,7 @@ In these cases, you can simply remove the row entirely.
 
 Most of the concepts I write about seem to come from the 70's and 80's, but differential privacy is a relatively new concept. It was first introduced in a paper from 2006 called [*Calibrating Noise to Sensitivity in Private Data Analysis*](https://desfontain.es/PDFs/PhD/CalibratingNoiseToSensitivityInPrivateDataAnalysis.pdf).
 
-The paper introduces the idea of adding noise to data to achieve privacy. Of course, adding noise to the dataset reduces its accuracy. Ɛ defines the amount of noise added to the dataset, with a small Ɛ meaning more privacy but less accurate data and vice versa. It's also referred to as the "privacy loss parameter".
+The paper introduces the idea of adding noise to data to achieve privacy. Of course, adding noise to the dataset reduces its accuracy. Ɛ defines the amount of noise added to the dataset, with a small Ɛ meaning more privacy but less accurate data and vice versa. It's also referred to as the "privacy loss parameter" or "privacy budget".
 
 #### Central Differential Privacy