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blog/posts/differential-privacy.md
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@ -23,6 +23,11 @@ It's useful to collect data from a large group of people. You can see trends in
Latanya Sweeney in a [paper](https://dataprivacylab.org/projects/identifiability/paper1.pdf) from 2000 used U.S. Census data to try and re-identify people solely based on the metrics available to her. She found that 87% of Americans could be identified based on only 3 metrics: ZIP code, date of birth, and sex. Latanya Sweeney in a [paper](https://dataprivacylab.org/projects/identifiability/paper1.pdf) from 2000 used U.S. Census data to try and re-identify people solely based on the metrics available to her. She found that 87% of Americans could be identified based on only 3 metrics: ZIP code, date of birth, and sex.
Obviously, being able to identify individuals based on publicly available data is a huge privacy issue.
## History ## 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) 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.