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| Homepage | http://www.thomas-steinke.net |
| https://twitter.com/shortstein |
| Homepage | http://www.thomas-steinke.net |
| https://twitter.com/shortstein |
I wrote some expository material about DP algorithms that go beyond the standard clip-and-add-noise paradigm:
The most well-known and widely-used method for achieving differential privacy is to compute the true function value \(f(x)\) and then add Laplace or Gaussian noise scaled to the global sensitivity of \(f\). This may be overly conservative. In this post we’ll show how we can do better.
[20] sounds like it has some interesting results, I should read that paper.
*checks references*
oh, it's my paper
Every year I feel a little bit more cynical, a little bit more exhausted with how the world works, a little bit more convinced that everyone else is insane.
If this trend continues for a few more decades, I'll become one of those senior researchers who now only studies "sentience" or something like that.
I used to wonder how that happens to people who used to do good work, but now I think I understand...
For the holidays, here's one of my favourite #maths facts: Legendre's Constant! Legendre gave his name to a number!
This number is 1.