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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.
It narrows the 10^3 - 10^6 penalty to 10x - 100x.
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.p…
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.
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.
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.
Earlier quoted context omitted.
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.
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.
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.
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/fed learning, but this stuff isn’t in the same zone.
I find it a bit embarrassing when Google publishes this stuff to be honest.
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…
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 needin…
It's very useful for things like healthcare data for example but also for products like "home assistants", and it's one of the use cases that the team hiring internally was giving in their job req. I tried to apply too but didn't get it unfortunately, this was a very interesting product to work on imho, congrats to whoever got my job :)
Earlier quoted context omitted.
With Fully Homomorphic Encryption it's nobody elses. The basic idea of of the project is to remove the need for trust.
To what end?
I think the most immediately useful is checking if a username/password hash appears in a leaked database without revealing which account or password hash is being checked.
Earlier quoted context omitted.
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…
You are very wrong about all of this btw.