Protection of data in ML pipelines through training set encryption, defense against data poisoning, membership inference attacks, and model inversion that could extract sensitive information. Covers differential privacy, secure multi-party computation for federated learning, sanitization of sensitive datasets, and data provenance control to prevent backdoor injection during training.