From a506f32cd0a375f0ef2386dab6cf93a54542d2ac Mon Sep 17 00:00:00 2001 From: fria <138676274+friadev@users.noreply.github.com> Date: Tue, 1 Jul 2025 07:09:32 -0500 Subject: [PATCH] update(blog)!: Privacy-Enhancing Technologies Series: Differential Privacy --- blog/posts/differential-privacy.md | 26 ++++++++++++++++++++++++++ 1 file changed, 26 insertions(+) create mode 100644 blog/posts/differential-privacy.md diff --git a/blog/posts/differential-privacy.md b/blog/posts/differential-privacy.md new file mode 100644 index 000000000..8e48a5407 --- /dev/null +++ b/blog/posts/differential-privacy.md @@ -0,0 +1,26 @@ +--- +date: + created: 2025-07-01T17:30:00Z +categories: + - Explainers +authors: + - fria +tags: + - Privacy Enhancing Technologies + - Differential Privacy +license: BY-SA +schema_type: BackgroundNewsArticle +description: | + Privacy Pass is a new way to privately authenticate with a service. Let's look at how it could change the way we use services. +--- +# Privacy-Enhancing Technologies Series: Differential Privacy + +Is it possible to collect data from a large group of people but protect each individual's privacy? In this entry of my series on privacy-enhancing technologies, we'll discuss differential privacy and how it can do just that. + +## Problem + +It's useful to collect data from a large group of people. You can see trends in a population. But it requires a lot of individual people to give up personally identifiable information. Even things that seem inocuous like your gender can help identify you. 87% of Americans can be identified by three pieces of information: + +## History + +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) \ No newline at end of file