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| -rw-r--r-- | posts/2019-03-14-great-but-manageable-expectations.md | 7 |
1 files changed, 3 insertions, 4 deletions
diff --git a/posts/2019-03-14-great-but-manageable-expectations.md b/posts/2019-03-14-great-but-manageable-expectations.md index 95cf5f2..5ed5134 100644 --- a/posts/2019-03-14-great-but-manageable-expectations.md +++ b/posts/2019-03-14-great-but-manageable-expectations.md @@ -521,14 +521,13 @@ Tensorflow implementation The DP-SGD is implemented in [TensorFlow Privacy](https://github.com/tensorflow/privacy). In the following I discuss the package in the current state (2019-03-11). It is divided -into two parts: `optimizers` which implements the actual differentially -private algorithm, and [`analysis`](https://github.com/tensorflow/privacy/tree/master/privacy/analysis) which computes the privacy guarantee. +into two parts: [`optimizers`](https://github.com/tensorflow/privacy/tree/master/privacy/optimizers) which implements the actual differentially +private algorithms, and [`analysis`](https://github.com/tensorflow/privacy/tree/master/privacy/analysis) which computes the privacy guarantee. The `analysis` parts implements a privacy ledger that \"keeps a record of all queries executed over a given dataset for the purpose of computing privacy guarantees\". On the other hand, all the computation -is done in `rdp_accountant.py` -([link](https://github.com/tensorflow/privacy/blob/7e2d796bdee9b60dce21a82a397eefda35b0ac10/privacy/analysis/rdp_accountant.py). +is done in [`rdp_accountant.py`](https://github.com/tensorflow/privacy/blob/7e2d796bdee9b60dce21a82a397eefda35b0ac10/privacy/analysis/rdp_accountant.py). At this moment, `rdp_accountant.py` only implements the computation of the privacy guarantees for DP-SGD with Gaussian mechanism. In the following I will briefly explain the code in this file. |