  Mobile cloud offloading is indispensable for inference tasks based on
large-scale deep models. However, transmitting privacy-rich inference data to
the cloud incurs concerns. This paper presents the design of a system called
PriMask, in which the mobile device uses a secret small-scale neural network
called MaskNet to mask the data before transmission. PriMask significantly
weakens the cloud's capability to recover the data or extract certain private
attributes. The MaskNet is em cascadable in that the mobile can opt in to or
out of its use seamlessly without any modifications to the cloud's inference
service. Moreover, the mobiles use different MaskNets, such that the collusion
between the cloud and some mobiles does not weaken the protection for other
mobiles. We devise a {\em split adversarial learning} method to train a neural
network that generates a new MaskNet quickly (within two seconds) at run time.
We apply PriMask to three mobile sensing applications with diverse modalities
and complexities, i.e., human activity recognition, urban environment
crowdsensing, and driver behavior recognition. Results show PriMask's
effectiveness in all three applications.
