Top of article mentions 'In the previous post...' (which is an intro to homomorphic encryption)
That post is here: https://radicalrafi.github.io/posts/homomorphic-encryption/
Homomorphic encryption is interesting from a mathematics perspective, but in practical terms it seems like an awful lot of effort being invested to move even more computing off of your own devices and onto the "cloud."
I used HE in my informatics b.sc. thesis to do privacy preserving surveillance: Store data on multiple servers and need server majority to reconstruct (non-HE), perform face recognition on encrypted data and then use (S?)HE to query a database if that face is in it - of course without the db learning about the content of the face data. So, turns out just throwing some math on the problem works (I just applied some previous work; you know what they say about the shoulders of giants) and gives you the advantages of surveillance with less potential for abuse - but the necessary computational power is absurd :(
(And yeah, HE "noise" is a pain)
> c is odd if m = 1 c is even if m = 0 ( Yes 0 is even ).
If c is the ciphertext than can't someone simply mod 2 and "decrypt" it?
is this the same technology that Numerai uses... or is it multi party computation (https://mortendahl.github.io/2017/04/17/private-deep-learnin...) ?
I am unclear on this - it looks like it operates 1 but at a time, so if I have a sequence of encrypted bits I can do freq analysis. Clearly that is not the case, so what bone headed misunderstanding am I making?
The best way to encrypt any data is to make adversary think the data does not exist.
For everything else rubber hose cryptanalysis will work.