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| author | Yuchen Pei <me@ypei.me> | 2019-03-20 14:44:14 +0100 |
|---|---|---|
| committer | Yuchen Pei <me@ypei.me> | 2019-03-20 14:44:14 +0100 |
| commit | 82a451b59d11d54270e222abf7318dde964a9557 (patch) | |
| tree | 5fc3aa4321ba8d06bcf1dd37524e9a30f974e273 | |
| parent | 4b24491f193d345ec61e201738c79af8aed30dd6 (diff) | |
minor
| -rw-r--r-- | posts/2019-03-13-a-tail-of-two-densities.md | 2 |
1 files changed, 1 insertions, 1 deletions
diff --git a/posts/2019-03-13-a-tail-of-two-densities.md b/posts/2019-03-13-a-tail-of-two-densities.md index 65f1a0a..b588048 100644 --- a/posts/2019-03-13-a-tail-of-two-densities.md +++ b/posts/2019-03-13-a-tail-of-two-densities.md @@ -46,7 +46,7 @@ guarantees for composed subsampling queries in general, and for DP-SGD in partic I also compare these privacy guarantees. This post focuses on the mathematics of differential privacy, and should be -suitable for anyone with some knowledge of probability. +suitable for anyone familiar with probability theory. For how the subject discussed in this post is related to privacy, check out the [Wikipedia entry](https://en.wikipedia.org/wiki/Differential_privacy) or [Dwork-Roth 2013](https://www.cis.upenn.edu/~aaroth/privacybook.html). |