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Zero-Knowledge Proofs for Machine Learning

Machine learning has become increasingly prominent and is widely used in various applications in practice. Despite its great success, the integrity of machine learning predictions and accuracy is a rising concern. The reproducibility of machine learning models that are claimed to achieve high accuracy remains challenging, and the correctness and consistency of machine learning predictions in real products lack any security guarantees. We introduce some of our recent results on applying the cryptographic primitive of zero knowledge proofs to the domain of machine learning to address these issues. The protocols allow the owner of a machine learning model to convince others that the model computes a particular prediction on a data sample, or achieves a high accuracy on public datasets, without leaking any information about the machine learning model itself. We developed efficient zero knowledge proof protocols for decision trees, random forests and neural networks.

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