OpenAI published research on 18 October 2016 describing a method for training deep learning models on private data without exposing that data directly [source]. The technique, called "semi-supervised knowledge transfer," uses a teacher model trained on sensitive information to guide a student model that can be publicly released.

The approach addresses a fundamental tension in machine learning: models trained on private datasets—such as medical records or personal communications—can inadvertently memorize and reproduce that private information. The research proposes training a teacher model on the private data, then using that teacher to label a separate public dataset. A student model learns from these labels rather than the original private data.

The paper demonstrates the method on datasets including hospital discharge summaries and text predictions, showing that student models can achieve comparable accuracy to models trained directly on private data while reducing the risk of exposing individual records. The technique incorporates differential privacy guarantees, adding mathematical bounds on how much information about any single training example can be inferred from the model.

This work predates the widespread commercial deployment of large language models and reflects early efforts to address privacy concerns in AI systems. The research was conducted in collaboration with academic institutions and appeared during a period when OpenAI was primarily focused on publishing foundational research rather than consumer products.

The method described does not eliminate all privacy risks—the student model still learns patterns present in the private data, albeit indirectly. The paper acknowledges that stronger privacy guarantees require trade-offs in model accuracy. No specific implementation timeline or product integration was announced in the original publication.