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| -rw-r--r-- | posts/2019-03-14-great-but-manageable-expectations.md | 8 |
1 files changed, 4 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 889f674..ff2beee 100644 --- a/posts/2019-03-14-great-but-manageable-expectations.md +++ b/posts/2019-03-14-great-but-manageable-expectations.md @@ -588,8 +588,8 @@ DP-SGD with the Gaussian mechanism: 2. Example 1 (RDP for the Gaussian mechanism) -\> Claim 22 (Moment Composition Theorem) -\> Example 3 (Moment composition applied to the Gaussian mechanism) -3. Conjecture 0 (RDP for Gaussian mechanism with specific magnitudes - for subsampling rate) -\> Conjecture 3 (Moment Composition Theorem +3. Claim 26 (RDP for Gaussian mechanism with specific magnitudes + for subsampling rate) -\> Claim 28 (Moment Composition Theorem and translation to conventional DP) Which one is the best? @@ -727,7 +727,7 @@ Here is a list of what I think may be interesting topics or potential problems to look at, with no guarantee that they are all awesome untouched research problems: -1. Prove Conjecture 2 +1. Prove Conjecture 1 2. Find a theoretically definitive answer whether the methods in Part 1 or Part 2 yield better privacy guarantees. 3. Study the non-Gaussian cases, general or specific. Let $p$ be some @@ -735,7 +735,7 @@ untouched research problems: $L(p(y) || p(y + \alpha))$ for $|\alpha| \le 1$? Can you find anything better than Gaussian? For a start, perhaps the nice tables of Rényi divergence in Gil-Alajaji-Linder 2013 may be useful? -4. Find out how useful Conjecture 0 is. Perhaps start with computing +4. Find out how useful Claim 26 is. Perhaps start with computing the constant $C$ nemerically. 5. Help with [the aforementioned issue](https://github.com/tensorflow/privacy/issues/23) in the |