From 7a62a90097cba90a94b56e567850713c294673c2 Mon Sep 17 00:00:00 2001
From: fria <138676274+friadev@users.noreply.github.com>
Date: Tue, 1 Jul 2025 08:20:20 -0500
Subject: [PATCH] add more info on history

---
 blog/posts/differential-privacy.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/blog/posts/differential-privacy.md b/blog/posts/differential-privacy.md
index f554e33f..848d6eeb 100644
--- a/blog/posts/differential-privacy.md
+++ b/blog/posts/differential-privacy.md
@@ -29,5 +29,6 @@ Obviously, being able to identify individuals based on publicly available data i
 
 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.
+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".
 
+Importantly, differential privacy adds noise *before* it's analyzed. k-anonymity relies on trying to anonymize data *after* it's collected, so it leaves the possibility that not enough parameters are removed to ensure each indidual cannot be identified.
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