Before revision:  (i) We develop a differentially private mechanism that protects the privacy of the teacher's training dataset. (ii) We propose a proximal policy-optimization objective that enables the student to benefit from the demonstrations despite the perturbations of the privacy mechanism.
After revision:  (i) We develop a differentially private mechanism that protects the privacy of the teacher's privacy mechanism. The privacy-aware design of the algorithm makes it possible to kickstart the student's learning despite the perturbations of the privacy mechanism.