  Deep learning classifiers are crucial in the age of artificial intelligence.
The device-edge-based collaborative inference has been widely adopted as an
efficient framework for promoting its applications in IoT and 5G/6G networks.
However, it suffers from accuracy degradation under non-i.i.d. data
distribution and privacy disclosure. For accuracy degradation, direct use of
transfer learning and split learning is high cost and privacy issues remain.
For privacy disclosure, cryptography-based approaches lead to a huge overhead.
Other lightweight methods assume that the ground truth is non-sensitive and can
be exposed. But for many applications, the ground truth is the user's crucial
privacy-sensitive information. In this paper, we propose a framework of
Roulette, which is a task-oriented semantic privacy-preserving collaborative
inference framework for deep learning classifiers. More than input data, we
treat the ground truth of the data as private information. We develop a novel
paradigm of split learning where the back-end DNN is frozen and the front-end
DNN is retrained to be both a feature extractor and an encryptor. Moreover, we
provide a differential privacy guarantee and analyze the hardness of ground
truth inference attacks. To validate the proposed Roulette, we conduct
extensive performance evaluations using realistic datasets, which demonstrate
that Roulette can effectively defend against various attacks and meanwhile
achieve good model accuracy. In a situation where the non-i.i.d. is very
severe, Roulette improves the inference accuracy by 21\% averaged over
benchmarks, while making the accuracy of discrimination attacks almost
equivalent to random guessing.
