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Google is making private AI practical with homomorphic encryption

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Re: Google is making private AI practical with homomorphic encryption

#251

Post by fragmede »

bevekspldnw wrote: post #67
luckydata wrote: post #57

Earlier quoted context omitted.

You are very wrong about all of this btw.

You literally don’t know who I am or the roles I had. So unless you can tell me how many steps you were from Kent Walker and what you worked on I’m gonna bet a hell of a lot I know more than you. Edit to clarify my prior point: some of the technology makes it into the product, but the putative data protections do not. Why? Because there is always a work around, and ads legal will approve it every time.

The distance between Kent Walker and Thomas Kurian is what you should be asking.

Re: Google is making private AI practical with homomorphic encryption

#253

Post by hyusap »

HlessClaudesman wrote: post #223

"hotword" detection, is this basically sniffing encrypted data for words the authorities don't want you to speak? Seems counterproductive to their stated aim of privacy.

from what I understand, only the holder of the private key would be able to see if a hotword was detected or not. even if Google ran hotword detection over your data, they’d only get a encrypted output on if the hotword was detected or not

Re: Google is making private AI practical with homomorphic encryption

#254

Post by fragmede »

xg15 wrote: post #225
fragmede wrote: post #220

Earlier quoted context omitted.

because you lose your phone and don't have access to the account anymore.

Then the app can make a bog-standard encrypted-at-rest backup to somewhere and make all the computations on the device on the cleartext data. I don't see the need to do computations on the encrypted data here, which is what FHE would provide in addition to traditional encryption. > and don't have access to the account anymore. This would be trouble with or without FHE. Even if the backend wouldn't need to decrypt the…

Okay, so the platform becomes valuable to its users when it's able to suggest things like "based on millions of users, people with cycles like yours typically ovulate around day 16."

In order to do the data mining in order to make those kinds of claims, traditionally you'd need to have access to the data.

As you point out, encrypted-at-rest is solved. But what about when it's not at rest?

In-use and in-transit is when FHE kicks in. Sure, you could just do it locally, but then you miss out on the aggregate data mining. Not for advertisers, but because it helps women with their bodies. The compelling product claim is "we literally cannot read your period data." Not "we pinky swear not to" but "we actually really really actually can't!"

Re: Google is making private AI practical with homomorphic encryption

#255

Post by akoboldfrying »

Aozora7 wrote: post #240
akoboldfrying wrote: post #226

Earlier quoted context omitted.

When McDonalds introduced salads to their menu, purchases shot up -- but ~no one actually bought the salads. They just felt better about buying the same old unhealthy food from McDonalds when they knew that healthier options were available that they could potentially buy. This is the same kind of thing. The crazy overhead of FHE means it's not practical for almost any kind of real world computational work. But having…

I'll have you know I started picking salads instead of fries in combo meals the moment they became available. Not that I'm a frequent customer, either before or after.

Good on you and the other four or five people who do this! ;)

Re: Google is making private AI practical with homomorphic encryption

#256

Post by FartyMcFarter »

cantalopes wrote: post #115

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

How would advertising be implemented in the scenario of using this technique?

Re: Google is making private AI practical with homomorphic encryption

#257

Post by Chris2048 »

luckydata wrote: post #110
bevekspldnw wrote: post #67

Earlier quoted context omitted.

You literally don’t know who I am or the roles I had. So unless you can tell me how many steps you were from Kent Walker and what you worked on I’m gonna bet a hell of a lot I know more than you. Edit to clarify my prior point: some of the technology makes it into the product, but the putative data protections do not. Why? Because there is always a work around, and ads legal will approve it every time.

and you don't know who I am. I might have been closer to that work than you would know.

Why don't you say what they are wrong about then?

Re: Google is making private AI practical with homomorphic encryption

#259

Post by bilsbie »

I think this could be an effective privacy preserving approach:

Run the encoding and first and last layer of the LLM on the users machine. They could still get some idea of the content but really couldn’t easily get the actual text back.

Anyone know if that’s viable?

Re: Google is making private AI practical with homomorphic encryption

#260

Post by monster_truck »

sabretooth1405 wrote: post #16

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.

One of the biggest problems IMHO is that they aren't trying to usefully accelerate it on anything other than specialty hardware or 64+ core EYPCs so nobody gets to play with it at home.

ex: A 7900XTX barely gets 0.5 TOPS of u/i64 naively w/ hip-direct, 5-10s just to bootstrap!

I needed more throughput for non-crypto i64 diff eqs so I slopped up a lib that uses RNS & CRT w/ Int8 GEMM... it's good for ~3.9 TOPS (~90% theoretical peak of the RDNA3) at prod relevant FHE sizes (2048/4096). This lowers bootstrap time to 200-500ms. It was basically free real estate lol

It isn't done yet (not worth the heat in the summer), going to finish it in the fall. Have been accumulating cloud credits to do CDNA3/4 validation in the meantime (If anyone has some to offer do let me know!)

It's neat but very dry, uses semantic contracts so you tell it what kind of mult you need and it chooses the validated best backend. If you're doing lots of smaller ops (512, 1024) it will use custom WMMA/MFMA kernels, dual issue, and grouped dispatch to land >70x over hip-direct.

https://github.com/doublemover/RNS8/

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