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.
Google is making private AI practical with homomorphic encryption
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Re: Google is making private AI practical with homomorphic encryption
#42I'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.
Re: Google is making private AI practical with homomorphic encryption
#43My 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.
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> 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]
Re: Google is making private AI practical with homomorphic encryption
#45Re: Google is making private AI practical with homomorphic encryption
#46Encryption or not, if it's on somebody else's server, it isn't yours. I don't believe Google has my best interest.
Re: Google is making private AI practical with homomorphic encryption
#47This is the same Google that doesn't have e2ee on their password manager by default. Like WTF, it's a password manager.
> 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
#48Proper 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.
Re: Google is making private AI practical with homomorphic encryption
#49My 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.
Re: Google is making private AI practical with homomorphic encryption
#50My 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.
"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.)