fully-homomorphic-encryption VS differential-privacy

Secure computing. This includes Fully Homomorphic Encryption AKA FHE, of which there is a language/compiler which just got released and Google's older FHE compiler. FHE is probably more "compiler" than "type system", e.g. Google's compiler works on C++. Also Security Type Systems which include Oblivious data structures and Oblivious ADTs.

I don't have any advice for you, but I do work on homomorphic encryption at Google and we have an FHE compiler project [1] (though it is likely going to change a lot in the coming year). I happen to have a math PhD, so the transition to this field was not a huge stretch, but before that I worked in supply chain optimization for data centers, and just decided this was too exciting to pass up.

[1]: https://github.com/google/fully-homomorphic-encryption/issue...

+1, and some compilers already exist to do that for you. See, e.g., Google's compiler (which I work on). https://github.com/google/fully-homomorphic-encryption

Once the tools are written, you should be able to take a program written in some language foo and transpile it to a FHE version of foo. See Google's C++ to FHE-C++ transpiler. Thus, you can test/debug your application without FHE before transpiling to something that is FHE.

An example is probably easier :) I quote here the description of the Google's differential privacy example:

GitHub: https://github.com/google/differential-privacy/tree/main/learning/clustering

Maybe it is not so radical. The original, pre-web internet was not client-server. Each end of the connection potentially had something the other wanted. IMO, that's a truer representation of the real world. Today's internet is entirely web and mobile app centric, as if the world is nothing more than a feedlot, with only a small number of large scale "farmers".

https://github.com/google/differential-privacy/blob/main/exa...

One of the most durable techniques to protect user privacy is through differential privacy. In a previous post, we explored how to build an Apache Beam pipeline that extracted and counted ngrams from HackerNews comments. Today, we'll take the same pipeline and upgrade it with some differential privacy goodness using Privacy-on-Beam from Google's differential privacy library.