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, but either Gemini era being the new money maker there’s no way this is being used by 99.9999999999% of users.
I find it a bit embarrassing when Google publishes this stuff to be honest.
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.)
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?
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.
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.
How do you make e2e password management work on multiple devices as convenient as not? You cannot.
Most people who use Google trust them. And if you do not trust them then why are you using their password manager? You see? Hence why they do not bother.
Of course one might read this and say, multi device e2ee is possible! Sure, but can you allow for recovery when people inevitably lose their recovery codes at scale? If you really care about this it is not necessary to go with Google.
Has FHE really progressed so far that it's now so efficient that doing computation on an encrypted prompt is feasible? I thought even basic operations like FHE addition were still thousands of times more complex. The only mention in the article I see is:
> But while homomorphic encryption has a nontrivial cost overhead, it shifts the capability/privacy trade-off to a question of cost. And the cost of homomorphic encryption is rapidly decreasing.
Which doesn't spell out exactly hon "nontrivial" the cost overhead still is.
> 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]
Secure Enclaves are broken all the time though [1]. They’re also not secure against attacks from a quantum computer (including store now decrypt later attacks). At least with symmetric FHE, anything that happens after encryption requires zero trust.
Maybe I'm not understanding this, but how is it that you can know enough about the data to process it without undermining the fundamental concept of encryption? Isn't encrypted data supposed to be just random noise without the key? The more you know about the underlying data the easier it gets to decrypt? Does this mean someone can just steal your encrypted data and use that to steal your identity without even needing to decrypt it anymore?
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).
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.
This kind of attitude is really disrespectful of decades of progress in cryptography. Without even considering homomorphic encryption, classic encryption is specifically designed to make intermediate nodes such as ISP dumb pipes that do not know the contents of communication. The ISP can store your communications on their server however they want.
If you don’t agree with this model, I’m afraid modern cryptography doesn’t have anything to offer.
Even if your files are properly encrypted, if you keep them on, say, Google Drive, then you might lose access to them if your account gets inadvertently blocked.
I think GP is trying to say something along those lines. Not that encryption isn't trustworthy, but that even with homomorphic encryption you still give up autonomy.
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.
Why should there by any reason to look at the above ones? We are talking about this B2C company which effects a lot of our lives. The discussion isn't about that or this. The discussion is about JUST this specific company.
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.
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.
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.
Hmm, could one instead of sending the model weights, send like, a merkle tree root for them, not exactly specifying the model, but at least demonstrating that the same model is used each time?
Correct. I appreciate the theoretical technology here, but I believe a great deal of harm is done by the fact that people are not likely to understand exactly what this means.
Which is to say, I believe that google is strongly implying the falsehood of "no one at Google can read your stuff."
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 didn’t say that it “appears” indistinguishable from noise, but that it is indistinguishable.
It seems like you strongly believe otherwise, but I suspect you don’t have a good reason to, and just find it unbelievable.
Do you think you can distinguish it from noise, if given an implementation and the information an adversary would have access to?
Of course, you not being able to wouldn’t demonstrate that noöne can. But, it seems like if you had a good reason to believe that an adversary can, that would suggest you might have some idea of how they could do so. And, if you do have such an idea, then, if that idea works, it would be important for others to know, and if it doesn’t, it would presumably benefit your understanding to see why it doesn’t.
Eh? With secret sharing one can do computation on a shared secret where it is provable that no individual party can recover any information about the data with their share alone.
I don’t see why you conclude that FHE couldn’t be close to as secure as that. (Like, not information theoretically, but with computationally bounded adversaries.)
You said that FHE is an oxymoron, seemingly on the basis that in proper encryption the ciphertext is indistinguishable from noise, and you think this can’t be true of FHE. I am arguing to the contrary.
In the secret sharing multi-party computation schemes, the individual shares of the secret are random and have no information about the plaintext.
I see no reason that FHE can’t have ciphertexts indistinguishable from noise.
No, that’s not what proper encryption means. Security for encryption means that cipher texts encrypting distinct messages are indistinguishable. This is called IND-CPA, and FHE satisfies this.
How can it possibly pass indcpa. If the model can give me any valuable information about the cipher. Apparently the middle man would know precisely what is contained in the payload.
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, but either Gemini era being the new money maker there’s no way this is being used by 99.9999999999% of users.
I find it a bit embarrassing when Google publishes this stuff to be honest.
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.)
Do you think that’s like a fundamental limit or something that will improve with time and new algorithms?
That's the reason for HEIR like optimization and parameter selection.
It narrows the 10^3 - 10^6 penalty to 10x - 100x.
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?
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.
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.
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.
How do you make e2e password management work on multiple devices as convenient as not? You cannot.
Most people who use Google trust them. And if you do not trust them then why are you using their password manager? You see? Hence why they do not bother.
Of course one might read this and say, multi device e2ee is possible! Sure, but can you allow for recovery when people inevitably lose their recovery codes at scale? If you really care about this it is not necessary to go with Google.
I stand by my original comment:
The average user does not want it.
Has FHE really progressed so far that it's now so efficient that doing computation on an encrypted prompt is feasible? I thought even basic operations like FHE addition were still thousands of times more complex. The only mention in the article I see is:
> But while homomorphic encryption has a nontrivial cost overhead, it shifts the capability/privacy trade-off to a question of cost. And the cost of homomorphic encryption is rapidly decreasing.
Which doesn't spell out exactly hon "nontrivial" the cost overhead still is.
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/
Secure Enclaves are broken all the time though [1]. They’re also not secure against attacks from a quantum computer (including store now decrypt later attacks). At least with symmetric FHE, anything that happens after encryption requires zero trust.
[1] https://sgx.fail/
Google is making private AI practical with Gemma4 something that you can run without an Internet connection.
All of the proofs of privacy rely on us getting the math right. All of the privacy from unplugging your internet cable is there by default.
Maybe I'm not understanding this, but how is it that you can know enough about the data to process it without undermining the fundamental concept of encryption? Isn't encrypted data supposed to be just random noise without the key? The more you know about the underlying data the easier it gets to decrypt? Does this mean someone can just steal your encrypted data and use that to steal your identity without even needing to decrypt it anymore?
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).
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.
Encryption or not, if it's on somebody else's server, it isn't yours. I don't believe Google has my best interest.
This kind of attitude is really disrespectful of decades of progress in cryptography. Without even considering homomorphic encryption, classic encryption is specifically designed to make intermediate nodes such as ISP dumb pipes that do not know the contents of communication. The ISP can store your communications on their server however they want.
If you don’t agree with this model, I’m afraid modern cryptography doesn’t have anything to offer.
Even if your files are properly encrypted, if you keep them on, say, Google Drive, then you might lose access to them if your account gets inadvertently blocked.
I think GP is trying to say something along those lines. Not that encryption isn't trustworthy, but that even with homomorphic encryption you still give up autonomy.
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.
There's more to life than branding.
so what's the option ??? Meta ?? xAi ?? or OpenAI ???
Why should there by any reason to look at the above ones? We are talking about this B2C company which effects a lot of our lives. The discussion isn't about that or this. The discussion is about JUST this specific company.
To what end?
It can give you confidentiality without availability. Not sure about integrity? Anyways, still useful.
It's FHE for "cryptographically-secure private AI inference" not for every other service where they snoop into your behavioral information.
Anything to keep people from running local models
Zama.ai is also a player in this space
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
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/
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.
[0] https://0xparc.org/blog/programmable-cryptography-1
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.
If this were the case it would be necessary to send the entire model weights in response to every request which would be a bit inconvenient.
Hmm, could one instead of sending the model weights, send like, a merkle tree root for them, not exactly specifying the model, but at least demonstrating that the same model is used each time?
Correct. I appreciate the theoretical technology here, but I believe a great deal of harm is done by the fact that people are not likely to understand exactly what this means.
Which is to say, I believe that google is strongly implying the falsehood of "no one at Google can read your stuff."
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.
They didn’t say that it “appears” indistinguishable from noise, but that it is indistinguishable.
It seems like you strongly believe otherwise, but I suspect you don’t have a good reason to, and just find it unbelievable.
Do you think you can distinguish it from noise, if given an implementation and the information an adversary would have access to?
Of course, you not being able to wouldn’t demonstrate that noöne can. But, it seems like if you had a good reason to believe that an adversary can, that would suggest you might have some idea of how they could do so. And, if you do have such an idea, then, if that idea works, it would be important for others to know, and if it doesn’t, it would presumably benefit your understanding to see why it doesn’t.
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)
Eh? With secret sharing one can do computation on a shared secret where it is provable that no individual party can recover any information about the data with their share alone.
I don’t see why you conclude that FHE couldn’t be close to as secure as that. (Like, not information theoretically, but with computationally bounded adversaries.)
I'm not saying that you can't design a system for secure cloud computing.
You said that FHE is an oxymoron, seemingly on the basis that in proper encryption the ciphertext is indistinguishable from noise, and you think this can’t be true of FHE. I am arguing to the contrary.
In the secret sharing multi-party computation schemes, the individual shares of the secret are random and have no information about the plaintext.
I see no reason that FHE can’t have ciphertexts indistinguishable from noise.
No, that’s not what proper encryption means. Security for encryption means that cipher texts encrypting distinct messages are indistinguishable. This is called IND-CPA, and FHE satisfies this.
How can it possibly pass indcpa. If the model can give me any valuable information about the cipher. Apparently the middle man would know precisely what is contained in the payload.