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blog/posts/differential-privacy.md

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 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 individual cannot be identified.
+Importantly, differential privacy adds noise *before* it's analyzed. k-anonymity (1) relies on trying to anonymize data *after* it's collected, so it leaves the possibility that not enough parameters are removed to ensure each individual cannot be identified.
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+1. k-anonymity means that for each row, at least k-1 other rows are identical.
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+### Google RAPPOR
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+In 2014, Google introduced [Randomized Aggregatable Privacy-Preserving Ordinal Response](https://arxiv.org/pdf/1407.6981) (RAPPOR), their [open source](https://github.com/google/rappor) implementation of differential privacy, with a few improvements.
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