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.
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).
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.
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.
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.
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."
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.
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.
> 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]
Private AI is practical by running the model locally, every much more so than any homomorphic encryption scheme.
So essentially the headline sells this as work to keep your data private, but really it's work to keep the AI-- which was trained on your code and your writing-- private.
I did some amount of research into the feasibility of PHE and FHE about 20 years ago, and my conclusion at the time was that the space overhead of the encrypted output was a massive bottleneck, which meant that while it was potentially useful in a small number of niche cases it wasn't ever going to be practical for general-purpose computations without a major breakthrough.
The gist was I could do an encrypted (int)x + (int)y = (int)z computation, I could encrypt the inputs and then get a result back that was correct, secure, and decryptable, but was like 1MB in size.
So, for someone whose knowledge is 20 years outdated and is about Pallier crypto, has that major breakthrough happened?
Great, private AI, at the cost of >1000x the resource usage. Because apparently AI companies weren't already using quite enough energy to cook the planet.
The most private AI is the one running on my own hardware, not in some giant data center.
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.
It's a step to provide targeted advertising with mathematically provable "no sensitive info stored" approach. The google must fight really hard because this is the only source of income that makes sense for their position. And it /is/ quite evil tbh
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[ 0.31 ms ] story [ 17.2 ms ] threadIf you don’t agree with this model, I’m afraid modern cryptography doesn’t have anything to offer.
Besides, FHE is not always about using somebody else's server. At Belfort, in addition to FHE acceleration, we also explore such uses cases; https://belfortlabs.com/blog/encrypted-fraud-detection-with-... https://belfortlabs.com/blog/belfort-partners-with-lg-on-enc...
I want to read a whitepaper but all I can find is the tl;dw conference presentation
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.
Which is to say, I believe that google is strongly implying the falsehood of "no one at Google can read your stuff."
> 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.
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.
> 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/
Reality: our computers will be used as distributed AI calculators
So essentially the headline sells this as work to keep your data private, but really it's work to keep the AI-- which was trained on your code and your writing-- private.
The gist was I could do an encrypted (int)x + (int)y = (int)z computation, I could encrypt the inputs and then get a result back that was correct, secure, and decryptable, but was like 1MB in size.
So, for someone whose knowledge is 20 years outdated and is about Pallier crypto, has that major breakthrough happened?
The most private AI is the one running on my own hardware, not in some giant data center.
I don't understand why we need to bring LGBTQ+ into everything
- 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.