An introduction to zero-knowledge machine learning
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An introduction to zero-knowledge machine learning
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Re: An introduction to zero-knowledge machine learning
#2A 17 million parameter model (~Resnet50) takes more than 50s proof time. Is this on top of the inference time?
I can see some niche applications for this system, but I am very skeptical it's ability to handle larger models (100M+) and the ability to and it's scalability when there are increased demand.
Re: An introduction to zero-knowledge machine learning
#3Just posted another thread from an article of a16z and a Zcash tweet: https://news.ycombinator.com/item?id=35457720
Re: An introduction to zero-knowledge machine learning
#4A 17 million parameter model (~Resnet50) takes more than 50s proof time. Is this on top of the inference time? I can see some niche applications for this system, but I am very skeptical it's ability to handle larger models (100M+) and the ability to and it's scalability when there are increased demand.
ZK is currently stuck with arithmetic circuit representations which are predictably very expensive to use as a representation for tensor data.
The matrix based formulations are still limited and don't play nicely with the parts of ML models which go beyond simple matrix multiplication.
I suspect someone will unify the two threads of research eventually, but it doesn't seem like it's there yet.
(FHE ML is even further away)
Re: An introduction to zero-knowledge machine learning
#5[deleted]
Re: An introduction to zero-knowledge machine learning
#6I'm very confused by the use case here, and this doesn't make sense to me:
> A good example of this would be applying a machine learning model on some sensitive data where a user would be able to know the result of model inference on their data without revealing their input to any third party (e.g., in the medical industry).
I don't get why I would care that the answer was generated specifically by GPT4. It sounds like they're billing this as some sort of "run a model on input with homomorphic encryption" but that doesn't really sound possible, and to the extent that it is I don't think you could ever convince me that the people managing the model on the GPU couldn't get access to both the plaintext input and plaintext output.
The way to get this kind of security is both simple and hard: make models that can run on consumer hardware.
Re: An introduction to zero-knowledge machine learning
#7I'm very confused by the use case here, and this doesn't make sense to me: > A good example of this would be applying a machine learning model on some sensitive data where a user would be able to know the result of model inference on their data without revealing their input to any third party (e.g., in the medical industry). I don't get why I would care that the answer was generated specifically by GPT4. It sounds li…
> make models that can run on consumer hardware.
This will not be hard at all in 10-20 years given the pace of semiconductor FLOPS per watt improvement. https://en.wikipedia.org/wiki/Koomey%27s_law
The neural engine in the A16 bionic on the latest iPhones can perform 17 TOPS. The A100 is about 1250 TOPS. Both these performance metrics are very subject to how you measure them, and I'm absolutely not sure I'm comparing apples to bananas properly.
However, we'd expect the iPhone has reached its maximum thermal load. So without increasing power use, it should match the A100 in about 6 to 7 doublings, which would be about 11 years. In 20 years the iPhone would be expected to reach the performance of approximately 1000 A100's.
At which point anyone will be able to train a GPT-4 in their pocket in a matter of days.
Re: An introduction to zero-knowledge machine learning
#8[dead]
Re: An introduction to zero-knowledge machine learning
#9I'm very confused by the use case here, and this doesn't make sense to me: > A good example of this would be applying a machine learning model on some sensitive data where a user would be able to know the result of model inference on their data without revealing their input to any third party (e.g., in the medical industry). I don't get why I would care that the answer was generated specifically by GPT4. It sounds li…
One large use case of ML and ZKML is Verifiable Computing. You can have an IoT device be able to enforce an untrusted super computer to process it's data on a certain program in a correct manner.
Re: An introduction to zero-knowledge machine learning
#10If Facebook releases Llama, and updated models thereafter, for purchase or as freeware, there will not really be as much need for this since everything will happen safely, locally, no?
It would be cool to see Meta release a 7B parameter as shareware, and subsequent larger models for a fee.
Edit: To be clear, I'm all for ZK, generally!