  Edge-cloud collaborative inference empowers resource-limited IoT devices to
support deep learning applications without disclosing their raw data to the
cloud server, thus preserving privacy. Nevertheless, prior research has shown
that collaborative inference still results in the exposure of data and
predictions from edge devices. To enhance the privacy of collaborative
inference, we introduce a defense strategy called PrivaScissors, which is
designed to reduce the mutual information between a model's intermediate
outcomes and the device's data and predictions. We evaluate PrivaScissors's
performance on several datasets in the context of diverse attacks and offer a
theoretical robustness guarantee.
