cryptography

eprint.iacr.org/2024/639.pdf 

This study demonstrates that a MLaaS operator can provide consumers with cryptographic techniques ensuring computational integrity with minimal assumptions.

- Consumers can be highly confident that the task is performed correctly

Packaged with a CUDA-compatible GPU computing backend, RISC-ZERO ZKVM significantly improves the computation time for DP-regression training. However, it comes at the cost of nearly exhausting the memory of the tested graphics unit (an 8GB NVIDIA GeForce RTX 4070 Laptop CPU).

After training on 60,000 examples, the difference in mean absolute error between both regressions is extremely low. Standard errors are measured for both regressions, with a very low delta of 0.00001.

This demonstrates that a privacy-preserving model can achieve nearly the same performance in predictions compared to a non-privacy-preserving model.

These are actually the notes I took while reading this article!

They mitigate this by partitioning the dataset into 343 blocks and sequentially verifying each block with a single ZKVM. Thus, they verify approximately 55 minutes of DP-private regression training on 60,000 (x, y) examples with a single GPU.