I say this as a lover of FHE and the wonderful cryptography around it:
While it’s true that FHE schemes continue to get faster, they don’t really have hope of being comparable to plaintext speeds as long as they rely on bootstrapping. For deep, fundamental reasons, bootstrapping isn’t likely to ever be less than ~1000x overhead.
When folks realized they couldn’t speed up bootstrapping much more, they started talking about hardware acceleration, but it’s a tough sell at time when every last drop of compute is going into LLMs. What $/token cost increase would folks pay for computation under FHE? Unless it’s >1000x, it’s really pretty grim.
For anything like private LLM inference, confidential computing approaches are really the only feasible option. I don’t like trusting hardware, but it’s the best we’ve got!
I get that there is a big LLM hype, but is there really no other application for FHE? Like for example trading algorithms (not the high speed once) that you can host on random servers knowing your stuff will be safe or something similar?
Don't you think there is a market for people who want services that have provable privacy even if it costs 1,000 times more? It's not as big a segment as Dropbox but I imagine it's there.
You're not joking. If you're like most people and have only a few TiB of data in total, self hosting on a NAS or spare PC is very viable. There are even products for non-technical people to set this up (e.g. software bundled with a NAS). The main barrier is having an ISP with a sufficient level of service.
there is, it's called governments. however this technology is so slow that using it in mission critical systems (think communication / coordinates during warfare) that it is not feasible IMO.
the parent post is right, confidential compute is really what we've got.
E2EE git was invented. I asked the creator if server can enforce protected branches or force pushes. He has no solution for evil clients. Maybe this could lead to E2EE Github?
I think the opening example involving Google is misleading. When I hear "Google" I think "search the web".
The articles is about getting an input encrypted with key k, processing it without decrypting it, and sending back an output that is encrypted with key k, too. Now it looks to me that the whole input must be encrypted with key k. But in the search example, the inputs include a query (which could be encrypted with key k) and a multi-terabyte database of pre-digested information that's Google's whole selling point, and there's no way this database could be encrypted with key k.
In other words this technique can be used when you have the complete control of all the inputs, and are renting the compute power from a remote host.
Not saying it's not interesting, but the reference to Google can be misunderstood.
I get the "client side" of this equation; some number of users want to keep their actions/data private enough that they are willing to pay for it.
What I don't think they necessarily appreciate is how expensive that would be, and consequently how few people would sign up.
I'm not even assuming that the compute cost would be higher than currently. Let's leave aside the expected multiples in compute cost - although they won't help.
Assume, for example, a privacy-first Google replacement. What does that cost? (Google revenue is a good place to start that Calc.) Even if it was say $100 a year (hint; it's not) how many users would sign up for that? Some sure, but a long long way away from a noticeable percentage.
Once we start adding zeros to that number (to cover the additional compute cost) it gets even lower.
While imperfect, things like Tor provide most of the benefit, and cost nothing. As an alternative it's an option.
I'm not saying that HE is useless. I'm saying it'll need to be paid for, and the numbers that will pay to play will be tiny.
An FHE Google today would be incredible expensive and incredibly slow. No one would pay for it.
The key question I think is how much computing speed will improve in the future. If we assume FHE will take 1000x more time, but hardware also becomes 1000x faster, then the FHE performance will be similar to today's plaintext speed.
Predicting the future is impossible, but as software improves and hardware becoming faster and cheaper every year, and as FHE provides a unique value of privacy, it's plausible that at some point it can become the default (if not 10 years, maybe in 50 years).
Today's hardware is many orders of magnitudes faster compared to 50 years ago.
There are of course other issues too. Like ciphertext size being much larger than plaintext, and requirement of encrypting whole models or indexes per client on the server side.
FHE is not practical for most things yet, but its venn diagram of feasible applications will only grow. And I believe there will be a time in the future that its venn diagram covers search engines and LLMs.
> If we assume FHE will take 1000x more time, but hardware also becomes 1000x faster, then the FHE performance will be similar to today's plaintext speed
Yeah but this also means you can do 1000x more things on plaintext.
A very basic way of how it works: encryption is basically just a function e(m, k)=c. “m” is your plaintext and “c” is the encrypted data. We call it an encryption function if the output looks random to anyone that does not have the key
If we could find some kind of function “e” that preserves the underlying structure even when the data is encrypted you have the outline of a homomorphic system. E.g. if the following happens:
e(2,k)*e(m,k) = e(2m,k)
Here we multiplied our message with 2 even in its encrypted form. The important thing is that every computation must produce something that looks random, but once decrypted it should have preserved the actual computation that happened.
It’s been a while since I did crypto, so google might be your friend here; but there are situations when e.g RSA preserves multiplication, making it partially homomorphic.
a simple example of partial homomorphic encryption (not full), would be if a system supports addition or multiplication. You know the public key, and the modulus, so you can respect the "wrap around" value, and do multiplication on an encrypted number.
other ones I imagine behave kinda like translating, stretching, or skewing a polynomial or a donut/torus, such that the point/intercepts are still solveable, still unknown to an observer, and actually represent the correct mathematical value of the operation.
just means you treat the []byte value with special rules
Thank you. So based on your examples it sounds like the "computation" term is quite literal. How would this apply at larger levels of complexity like interacting anonymously with a database or something like that?
Assuming speed gets solved as predicted, for an application like search, the provider would have to sync a new database of “vectors” to all clients every time the index updates. On top of that, these DBs are tens if not hundreds of GB huge.
Ok, lets stop being delusional here. I'll tell you how this will actualy work:
Imagine your device sending Google an encrypted query and getting back the exact results it wanted — without you having any way of knowing what that query was or what result they returned. The technique to do that is called Fully Homomorphic Encryption (FHE).
I say this as a lover of FHE and the wonderful cryptography around it:
While it’s true that FHE schemes continue to get faster, they don’t really have hope of being comparable to plaintext speeds as long as they rely on bootstrapping. For deep, fundamental reasons, bootstrapping isn’t likely to ever be less than ~1000x overhead.
When folks realized they couldn’t speed up bootstrapping much more, they started talking about hardware acceleration, but it’s a tough sell at time when every last drop of compute is going into LLMs. What $/token cost increase would folks pay for computation under FHE? Unless it’s >1000x, it’s really pretty grim.
For anything like private LLM inference, confidential computing approaches are really the only feasible option. I don’t like trusting hardware, but it’s the best we’ve got!
I get that there is a big LLM hype, but is there really no other application for FHE? Like for example trading algorithms (not the high speed once) that you can host on random servers knowing your stuff will be safe or something similar?
Don't you think there is a market for people who want services that have provable privacy even if it costs 1,000 times more? It's not as big a segment as Dropbox but I imagine it's there.
???
For the equivalent of $500 in credit you could self host the entire thing!
You're not joking. If you're like most people and have only a few TiB of data in total, self hosting on a NAS or spare PC is very viable. There are even products for non-technical people to set this up (e.g. software bundled with a NAS). The main barrier is having an ISP with a sufficient level of service.
For most this would mean only specially treating a subset of all the sensitive data they have.
there is, it's called governments. however this technology is so slow that using it in mission critical systems (think communication / coordinates during warfare) that it is not feasible IMO.
the parent post is right, confidential compute is really what we've got.
For LLM inference, the market that will pay $20,000 for what is now $20 is tiny.
E2EE git was invented. I asked the creator if server can enforce protected branches or force pushes. He has no solution for evil clients. Maybe this could lead to E2EE Github?
https://news.ycombinator.com/item?id=44530927
I think the opening example involving Google is misleading. When I hear "Google" I think "search the web".
The articles is about getting an input encrypted with key k, processing it without decrypting it, and sending back an output that is encrypted with key k, too. Now it looks to me that the whole input must be encrypted with key k. But in the search example, the inputs include a query (which could be encrypted with key k) and a multi-terabyte database of pre-digested information that's Google's whole selling point, and there's no way this database could be encrypted with key k.
In other words this technique can be used when you have the complete control of all the inputs, and are renting the compute power from a remote host.
Not saying it's not interesting, but the reference to Google can be misunderstood.
I get the "client side" of this equation; some number of users want to keep their actions/data private enough that they are willing to pay for it.
What I don't think they necessarily appreciate is how expensive that would be, and consequently how few people would sign up.
I'm not even assuming that the compute cost would be higher than currently. Let's leave aside the expected multiples in compute cost - although they won't help.
Assume, for example, a privacy-first Google replacement. What does that cost? (Google revenue is a good place to start that Calc.) Even if it was say $100 a year (hint; it's not) how many users would sign up for that? Some sure, but a long long way away from a noticeable percentage.
Once we start adding zeros to that number (to cover the additional compute cost) it gets even lower.
While imperfect, things like Tor provide most of the benefit, and cost nothing. As an alternative it's an option.
I'm not saying that HE is useless. I'm saying it'll need to be paid for, and the numbers that will pay to play will be tiny.
An FHE Google today would be incredible expensive and incredibly slow. No one would pay for it.
The key question I think is how much computing speed will improve in the future. If we assume FHE will take 1000x more time, but hardware also becomes 1000x faster, then the FHE performance will be similar to today's plaintext speed.
Predicting the future is impossible, but as software improves and hardware becoming faster and cheaper every year, and as FHE provides a unique value of privacy, it's plausible that at some point it can become the default (if not 10 years, maybe in 50 years).
Today's hardware is many orders of magnitudes faster compared to 50 years ago.
There are of course other issues too. Like ciphertext size being much larger than plaintext, and requirement of encrypting whole models or indexes per client on the server side.
FHE is not practical for most things yet, but its venn diagram of feasible applications will only grow. And I believe there will be a time in the future that its venn diagram covers search engines and LLMs.
> If we assume FHE will take 1000x more time, but hardware also becomes 1000x faster, then the FHE performance will be similar to today's plaintext speed
Yeah but this also means you can do 1000x more things on plaintext.
> FHE enables computation on encrypted data
This is fascinating. Could someone ELI5 how computation can work using encrypted data?
And does "computation" apply to ordinary internet transactions like when using a REST API, for example?
A very basic way of how it works: encryption is basically just a function e(m, k)=c. “m” is your plaintext and “c” is the encrypted data. We call it an encryption function if the output looks random to anyone that does not have the key
If we could find some kind of function “e” that preserves the underlying structure even when the data is encrypted you have the outline of a homomorphic system. E.g. if the following happens:
e(2,k)*e(m,k) = e(2m,k)
Here we multiplied our message with 2 even in its encrypted form. The important thing is that every computation must produce something that looks random, but once decrypted it should have preserved the actual computation that happened.
It’s been a while since I did crypto, so google might be your friend here; but there are situations when e.g RSA preserves multiplication, making it partially homomorphic.
a simple example of partial homomorphic encryption (not full), would be if a system supports addition or multiplication. You know the public key, and the modulus, so you can respect the "wrap around" value, and do multiplication on an encrypted number.
other ones I imagine behave kinda like translating, stretching, or skewing a polynomial or a donut/torus, such that the point/intercepts are still solveable, still unknown to an observer, and actually represent the correct mathematical value of the operation.
just means you treat the []byte value with special rules
Thank you. So based on your examples it sounds like the "computation" term is quite literal. How would this apply at larger levels of complexity like interacting anonymously with a database or something like that?
Assuming speed gets solved as predicted, for an application like search, the provider would have to sync a new database of “vectors” to all clients every time the index updates. On top of that, these DBs are tens if not hundreds of GB huge.
Ok, lets stop being delusional here. I'll tell you how this will actualy work:
Imagine your device sending Google an encrypted query and getting back the exact results it wanted — without you having any way of knowing what that query was or what result they returned. The technique to do that is called Fully Homomorphic Encryption (FHE).
queries are Oblivious Transfer - a second limited case of FHE that actually addresses the filter threat model.