To establish strong privacy guarantees , it is important to limit the student 's access to its teachers , so that the student 's exposure to teachers ' knowledge can be meaningfully quantified and bounded . Fortunately , there are many techniques for speeding up knowledge transfer that can reduce the rate of student / teacher consultation during learning . We describe several techniques in this paper , the most effective of which makes use of generative adversarial networks ( GANs ) ( Goodfellow et al . , 2014 ) applied to semi - supervised learning , using the implementation proposed by Salimans et al . ( 2016 ) . For clarity , we use the term PATE - G when our approach is combined with generative , semisupervised methods . Like all semi - supervised learning methods , PATE - G assumes the student has access to additional , unlabeled data , which , in this context , must be public or non - sensitive . This assumption should not greatly restrict our method 's applicability : even when learning on sensitive data , a non - overlapping , unlabeled set of data often exists , from which semi - supervised methods can extract distribution priors . For instance , public datasets exist for text and images , and for medical data .