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ZK Machine Learning - HackMD

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Smart contracts on the Ethereum blockchain extend the range of what is possible to define in code. However, the constraints of blockchain computation and the public nature of operations on the blockchain impede the expansion to compute heavy applications on private/sensitive data, such as machine learning. In this tutorial post, together with the associated repo and demo webapp, we explore how zero knowledge proofs can help lift these barriers by performing computations off-chain and providing a proof that this computation was correctly executed, while shielding private data. The proof can then be verified on-chain for a much smaller computational cost, enabling us to implement on-chain, private machine learning. For this demo, we focused on the implementation of a simple computer vision deep learning convolutional neural network for handwritten digit recognition (MNIST). To check out the demo, please follow the instructions in this github repo, or play with the webapp demo at: https:/

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