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zero knowledge proof for NN inference
Hi,
I'm a student who wants to use zksnark to verify the correctness of neural network's computation. And I'm trying to run your tutorial code on windows 10 64-bit.
I have installed rust 1.47.0 and python 3.8.5, and the python files Mnist_end_to_end.py and Mnist_end_to_end_quant.py work well. But I got the following error when running tutorial_shallownet_prover.rs.
I guess it's an overflow error in the following line but have no idea how to fix it.
Besides, I find that you have updated the code, but the document files are not updates. In your latest version, I cannot find tutorial_shallownet_prover.rs.
Looking forward to your reply! Thanks!
Hello, I switch rustc version to 1.47.0
, but when I run cargo run --bin gen_data
, it failed and showed this version of Cargo is older than the 2021 edition, and only supports 2015 and 2018 editions.
which version of rust
and cargo
should I use?
I run this command under file zk-ml-private-model-baseline
.
At least make sure cargo build
work successful.
Check here:
https://docs.travis-ci.com/user/languages/rust/
With the improvement on LCS, we can expect better runtime.
Hi, I have successfully run your tutorial codes. And I have two questions about ZEN.
Can ZEN support batch verification?
For example, a user wants to do several predictions on many test sets. In your tutorial code, it takes a test set one time and verify the proof. I am wondering whether your ZEN can verify many predictions in a batch, which is more efficient.
Can ZEN support encrypted neural network?
I find that the prover knows the model weights when generates proof. If a user (verifier) wants to protect the privacy of its model, it sends encrypted model and encrypted test data to a server (prover). The server computes and sends back the encrypted prediction result. The server also need to prove that it computes correctly.
In this situation, the model weights, the input and output are all encrypted. I am confused whether zkSNARK or your ZEN still works in this situation.
Looking forward to your reply. Thanks!
A tutorial to teach a potential user how to use zen, eg:
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