  Knowledge distillation (KD) has been widely used for model compression and
knowledge transfer. Typically, a big teacher model trained on sufficient data
transfers knowledge to a small student model. However, despite the success of
KD, little effort has been made to study whether KD leaks the training data of
the teacher model. In this paper, we experimentally reveal that KD suffers from
the risk of privacy leakage. To alleviate this issue, we propose a novel
knowledge distillation method, swing distillation, which can effectively
protect the private information of the teacher model from flowing to the
student model. In our framework, the temperature coefficient is dynamically and
adaptively adjusted according to the degree of private information contained in
the data, rather than a predefined constant hyperparameter. It assigns
different temperatures to tokens according to the likelihood that a token in a
position contains private information. In addition, we inject noise into soft
targets provided to the student model, in order to avoid unshielded knowledge
transfer. Experiments on multiple datasets and tasks demonstrate that the
proposed swing distillation can significantly reduce (by over 80% in terms of
canary exposure) the risk of privacy leakage in comparison to KD with
competitive or better performance. Furthermore, swing distillation is robust
against the increasing privacy budget.
