From 9a4f49a2b3f66cb8f64aed906b3d80d5bef516dc Mon Sep 17 00:00:00 2001
From: fria <138676274+friadev@users.noreply.github.com>
Date: Mon, 7 Jul 2025 10:39:55 -0500
Subject: [PATCH] add intro before differential privacy

---
 blog/posts/differential-privacy.md | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/blog/posts/differential-privacy.md b/blog/posts/differential-privacy.md
index 811a9b3fd..99683778b 100644
--- a/blog/posts/differential-privacy.md
+++ b/blog/posts/differential-privacy.md
@@ -29,6 +29,12 @@ Obviously, being able to identify individuals based on publicly available data i
 
 ### Before Differential Privacy
 
+Being able to collect aggregate data is essential for research. It's what the U.S. Census does every 10 years.
+
+Usually we're more interested in the data as a whole and not data of individual people as it can show trends and overall patterns in groups of people. However, in order to get that data we must collect it from individuals.
+
+It was thought at first that simply removing names and other obviously identifying details from the data was enough to prevent re-identification, but [Latanya Sweeney](https://latanyasweeney.org/JLME.pdf) (a name that will pop up a few more times) proved in 1997 that even without names, a significant portion of individuals can be re-identified from a dataset by cross-referencing external data.
+
 Previous attempts at anonymizing data have relied on been highly vulnerable to reidentification attacks.
 
 #### AOL Search Log Release
@@ -43,6 +49,8 @@ Analyst [Nathan Ruser](https://x.com/Nrg8000/status/957318498102865920) indicate
 
 It was also possible to [deanonymize](https://steveloughran.blogspot.com/2018/01/advanced-denanonymization-through-strava.html) individual users in some circumstances.
 
+#### 
+
 ### Dawn of Differential Privacy
 
 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).