Google is making private AI practical with homomorphic encryption
blog.google33 points by u1hcw9nx an hour ago
33 points by u1hcw9nx an hour ago
My master's thesis is on a topic in this field (Privacy Preserving ML) and from my understanding HE and other techniques have very high overheads(~10^3) on inference tasks and thus aren't very commercially viable.
[delayed]
Proper encryption means the ciphertext is indistinguishable from noise. So...in order to be able to process on it, you have to make it not indistinguishable from noise.
So I take offense to the term FHE. It's a oxymoron.
The whole thing immidiatly stands out as a sham to build trust where it's gone.
they could have gone with an oblivious transfer approach (where it's working on what looks like multiple problems at once, you don't know which)
It sounds neat, but I do wonder how viable this is commercially. How high do we rate the chances that governments around the world will step in before another kind of E2E is rolled out.
"They" are "we". Private communication and private computation are the only way for the corporate cloud to actually help people without subjecting them to abuse.
One flaw with FHE is that it guarantees only that you need the key to see the inputs or outputs of the computation, but not necessarily that the computation is the one you want. For example, the computation could be adversarial for certain inputs, or an adversary could insert their own computation first (or last).
100% not an expert but my understanding was that part of what you are proving by signing the computation is that the computation itself was performed specifically as agreed to. I may be mixing this up with zero knowledge proofs.
Does this rely on the Trust Me Bro model, or is there some way for the client to verify that the provider actually isn't able to see your inputs?
I want to read a whitepaper but all I can find is the tl;dw conference presentation
Related, I'd seen this blog [0] posted on HN a few years back that gave a nice run down on the "programmable cryptography" space which introduce FHE and a few other neat concepts. Really enjoyed the read and learned some new terms.
The linked project page [1] claims to be fully homomorphic. Assuming the claim holds (I haven't verified it), then there is provably no way for Google or anyone else to obtain any information from the encrypted data or computation performed on them.
FHE is traditionally horrifically slow, so it's hard to imagine running anything beyond toy models with it. They list some applications on the original article page, but (presumably) they must be dramatically stripped down in order to run within any reasonable time budget. This is not going to run anything like a Sol/Opus any time soon.
[1]: https://heir.dev/
Encryption or not, if it's on somebody else's server, it isn't yours. I don't believe Google has my best interest.
With Fully Homomorphic Encryption it's nobody elses.
The basic idea of of the project is to remove the need for trust.
It's google. They are good at engineering. Not at creating trust. After the bizillionth time they have broken trust, there is no need for benefit of the doubt.
Yeah. Google is an ad business. Their entire motive for getting invested in AI is ad revenue. We're supposed to believe they just... won't turn on the money fountain? After going into the red for their data center investments? Hell nah.
I'd expect this to be something like the Google Ad ID: technically separated from what Google considers personal information, but trivially easy to tie back to an individual person and to other information about that person.
They're continually breaking trust by illegally scraping up the internet to feed to their plagiarism machine, which they are now asking us to trust with more data. It's not a compelling arguement.
It's FHE for "cryptographically-secure private AI inference" not for every other service where they snoop into your behavioral information.