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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

#41
post #39

Earlier quoted context omitted.

If you think about it for a moment you will realize the average user does not want that.

I suspect that your one moment of thought follows many years of contemplation. Maybe you can fill us in on some of that background.

E2E encryption means that if the user loses the keys, there is no way to recover that even if they contact support and prove the data belongs to them.

Re: Google is making private AI practical with homomorphic encryption

#42

I've published two papers on using HE for ML and it's nice, but also alarming, to see big players like Google and Microsoft making decent tools for performing HE. Although the technology is still much farther out from being commercially viable, it does pose an interesting problem about how these data aggregation companies will utilise a tech that is inherently private.

I guess I'm curious from your perspective what and why makes the tech from being commercially viable, and the concerns of big players adopting this (which was always going to happen I imagine, but when shapes a lot of the future of the field).

Re: Google is making private AI practical with homomorphic encryption

#43

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.

Exactly my concern, and worse overhead that what I recalled.

Cost-wise the only viable private compute is local compute. It's more expensive than cloud, but true private compute in the cloud is definitely pricier.

Re: Google is making private AI practical with homomorphic encryption

#44
Quoted from the blog post:

> user-data can be protected from data breaches, but then the service provider cannot provide features that depend on the data, such as spam or virus detection

I think they forgot "or advertising" at the end.

I don't trust Google. I would much prefer to use on-prem or - at most - one of the secure-enclave providers like Tinfoil[1] or Private Mode[2]

[1] https://tinfoil.sh/ [2] https://www.privatemode.ai/

Re: Google is making private AI practical with homomorphic encryption

#46

Encryption or not, if it's on somebody else's server, it isn't yours. I don't believe Google has my best interest.

It can give you confidentiality without availability. Not sure about integrity? Anyways, still useful.

Re: Google is making private AI practical with homomorphic encryption

#47

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 black or white.

Re: Google is making private AI practical with homomorphic encryption

#48
post #33
post #15

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.

This is actually the magic of FHE. The ciphertext is indistinguishable from noise AND can be computed on, it just looks like different noise. If you believe the underlying cryptographic hardness assumption of LWE/RLWE/etc, then yes Google cannot see any of the input or output of the model.

If it only appears indistinguishable from noise, but it's actually not, then it's just deception.

Re: Google is making private AI practical with homomorphic encryption

#49

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?

Re: Google is making private AI practical with homomorphic encryption

#50

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 article conspicuously fails to go into much detail about that. I poked around with an AI a bit (to rapidly cover all the linked pages) and it seems the best numbers we can get are from this arxiv paper: https://arxiv.org/html/2506.18150v4 Which says:

"We evaluate HE-LRM on UCI (health prediction) and Criteo (click prediction), achieving inference latencies of 24 seconds on UCI and 228 to 489 seconds, respectively, on a single-threaded CPU."

There don't seem to be any direct comparisons available, probably because nobody else has any reason to limit themselves to one single-threaded CPU with normal techniques, but for reference the AI seems to expect that normal times for conventional setups are in the milliseconds range, fairly comfortably, even on CPU. I didn't find a clean primary source to link to for this claim, but clicking through various things that don't cleanly state the situation it did seem plausible. So we seem to still be in the range of single-digit orders of magnitude slower, possibly as much as 5 or 6, which is to say, we're still talking the range where we need to take the log of the difference to get sensible numbers, we're not using percentages.

(To run it yourself, I basically just fed the URL from the HN link, mentioned that FHE is known to be slow, and asked if anything linked in the blog post gave concrete times.)

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