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-rw-r--r--posts/2019-03-13-a-tail-of-two-densities.md3
-rw-r--r--posts/2019-03-14-great-but-manageable-expectations.md3
2 files changed, 4 insertions, 2 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 579b13e..f7d6c91 100644
--- a/posts/2019-03-13-a-tail-of-two-densities.md
+++ b/posts/2019-03-13-a-tail-of-two-densities.md
@@ -35,7 +35,8 @@ I also attempt to reproduce a seemingly better moment bound for the
Gaussian mechanism with subsampling, with one intermediate step which I
am not able to prove.
-After that I explain the Tensorflow implementation of differential privacy,
+After that I explain the Tensorflow implementation of differential privacy
+in its [Privacy](https://github.com/tensorflow/privacy/tree/master/privacy) module,
which focuses on the differentially private stochastic gradient descent
algorithm (DP-SGD).
diff --git a/posts/2019-03-14-great-but-manageable-expectations.md b/posts/2019-03-14-great-but-manageable-expectations.md
index ce17986..554a7c4 100644
--- a/posts/2019-03-14-great-but-manageable-expectations.md
+++ b/posts/2019-03-14-great-but-manageable-expectations.md
@@ -17,7 +17,8 @@ I also attempt to reproduce a seemingly better moment bound for the
Gaussian mechanism with subsampling, with one intermediate step which I
am not able to prove.
-After that I explain the Tensorflow implementation of differential privacy,
+After that I explain the Tensorflow implementation of differential privacy
+in its [Privacy](https://github.com/tensorflow/privacy/tree/master/privacy) module,
which focuses on the differentially private stochastic gradient descent
algorithm (DP-SGD).