  Collaborative inference has recently emerged as an attractive framework for
applying deep learning to Internet of Things (IoT) applications by splitting a
DNN model into several subpart models among resource-constrained IoT devices
and the cloud. However, the reconstruction attack was proposed recently to
recover the original input image from intermediate outputs that can be
collected from local models in collaborative inference. For addressing such
privacy issues, a promising technique is to adopt differential privacy so that
the intermediate outputs are protected with a small accuracy loss. In this
paper, we provide the first systematic study to reveal insights regarding the
effectiveness of differential privacy for collaborative inference against the
reconstruction attack. We specifically explore the privacy-accuracy trade-offs
for three collaborative inference models with four datasets (SVHN, GTSRB,
STL-10, and CIFAR-10). Our experimental analysis demonstrates that differential
privacy can practically be applied to collaborative inference when a dataset
has small intra-class variations in appearance. With the (empirically)
optimized privacy budget parameter in our study, the differential privacy
technique incurs accuracy loss of 0.476%, 2.066%, 5.021%, and 12.454% on SVHN,
GTSRB, STL-10, and CIFAR-10 datasets, respectively, while thwarting the
reconstruction attack.
