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Google is making private AI practical with homomorphic encryption

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Re: Google is making private AI practical with homomorphic encryption

#91

Post by michaelmrose »

u1hcw9nx wrote: post #51
sabretooth1405 wrote: post #16

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.

That's the reason for HEIR like optimization and parameter selection. It narrows the 10^3 - 10^6 penalty to 10x - 100x.

Which seems massively worse than a real local device in fact 2x is probably untenable to the point of uselessness because actually privacy sensitive matters need actual privacy that can't be defeated by your government telling Google to serve you compromised js and spy on you anyway and most people don't give 2 shits about privacy so they won't pay 10% more let alone 2x.

I'm glad people fund things that are only of interest to nerds but this will never be useful.

Re: Google is making private AI practical with homomorphic encryption

#93

Post by pamcake »

dhx wrote: post #63
sabretooth1405 wrote: post #16

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.

To throw out some real and up-to-date numbers from [1] for FHE at "128-bit security level", to sort 8x 8-bit unsigned integers on the most ordinary of desktop PCs, wait 3 seconds for the result. Want to sort 32x 8-bit unsigned integers instead? Come back 34 seconds later for the result. update: also see [2] for some primitive unsigned 64-bit integer operation benchmarks with the TFHE-rs library (winner in the sorting…

Benchmarking code in repo: https://github.com/google/heir/tree/main/benchmark

Project intro talk from 2023: https://www.youtube.com/watch?v=kqDFdKUTNA4

Re: Google is making private AI practical with homomorphic encryption

#94

Post by michaelmrose »

bitpush wrote: post #47
Cider9986 wrote: post #22

This is the same Google that doesn't have e2ee on their password manager by default. Like WTF, it's a password manager.

F. Scott Fitzgerald's test of top-tier intelligence - > Holding two opposing views in the mind means accepting two contradictory ideas at the same time without needing to pick one side or rush to a simple answer I continue to use Apple products because they are top class even though everytime I think of Tim Cook in the Oval Office presenting the gold plaque to the current president, it makes me wanna puke. World isnt…

[flagged]

Re: Google is making private AI practical with homomorphic encryption

#95

Post by pinkmuffinere »

hellohello2 wrote: post #92

Have all the skeptics in this thread somehow forgot about Moore's law?

What, how is that relevant? the transistors on a microchip double about every two years. That’s descriptive, not prescriptive, so whatever you’re trying to imply about the future is kindof a toss up — there’s no promise the parents hold. And, it doesn’t really have to do with tfa, as far as I can tell? Do you perhaps mean Murphy’s law, “whatever can go wrong will go wrong”?

Re: Google is making private AI practical with homomorphic encryption

#96

Post by Fordec »

sabretooth1405 wrote: post #16

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.

The primary path to speed ups appear to be in custom ASICs by startups like Niobium. Combined with the recent Taalas acquisition by AMD, I think I see where this is going.

But yeah, for hot path traffic it's probably going to be swamped by the input data rate. But I expected identity tables and cached lookup data will need to be a core component so duplicate checks is avoided in every way available.

Re: Google is making private AI practical with homomorphic encryption

#97

Post by gaigalas »

I have an idea for AI companies for tremendous scale, with privacy and all nice things accounted for:

- Unstarve the GPU and RAM consumer market.

- Let enthusiasts and volunteers quickly ramp up local AI.

- Reap the results that the community will most certainly achieve.

Don't repeat Microsoft's earlier mistakes. It flourished when it embraced the community and open source. If it had made that move earlier, it would have been unstoppable.

Yes, if you can control the entire market that's probably awesome. But it's also full of nonsense risks.

Re: Google is making private AI practical with homomorphic encryption

#98

Post by pamcake »

raggi wrote: post #88
froh wrote: post #86

Earlier quoted context omitted.

where does this factor "1000x" come from? I have doubts.

https://www.jeremykun.com/2024/05/04/fhe-overview/#the-highe... > Fourth, there is a bandwidth concern. FHE encryption schemes generally increase the size of the data being encrypted, and the user must send the server a special set of encryption keys to enable the computation, which are relatively large as well. The special keys need only be generated and sent once and can be used for all future computations, but the…

I'm still a bit skeptic - 1000x sounds overly optimistic and only looking at encryption transformation without any operation is already at least 10000x with scheme mentioned in that quote.

Sibling comment estimates lower bounds of current research at minimun 10^6 overhead which sounds more realistic.

Re: Google is making private AI practical with homomorphic encryption

#99

Post by jacquesm »

abetusk wrote: post #49
sabretooth1405 wrote: post #16

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.

1000x slowdown is bad but not a complete deal breaker. Do you have a sense of what a reasonable achievable factor is? Do you have sense for how long before we get to that achievable factor?

It's a ridiculous waste of energy, just use local compute.

Re: Google is making private AI practical with homomorphic encryption

#100

Post by bitpush »

bevekspldnw wrote: post #56
sabretooth1405 wrote: post #16

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.

Commercially viable for Google boils down to can they attribute ads behaviors to it or not. Then there’s a second tier of things that just make those wheels turn and if they do or don’t make ads revenue is nominally immaterial. The teams doing this stuff at Google are purely for show, none of this makes it into any real products. There’s the narrow exception of stuff like gboard, that does use privacy preserving ML/f…

Autonomous driving was all show until Waymo happened AI research was all show until transformers happened. Quantum research is all show until ..

and now this.

Part of changing the world involves imagining a changed-world.

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