The problem Privacy Preserving machine Learning using ZK-SNARK solves

Machine learning algorithms often require access to sensitive data in order to train models, such as medical records or financial information. This presents a challenge for individuals and organizations that want to protect the privacy of this data. Traditional approaches to privacy protection, such as data anonymization or encryption, may not be sufficient for some applications.
ZkSNARKs are a cryptographic tool that can be used to prove the validity of a computation without revealing any of the inputs or intermediate steps. This makes them an attractive solution for privacy-preserving machine learning, as they allow data to be kept private while still enabling the training of accurate models.