  Personal sensory data is used by context-aware mobile applications to provide
utility. However, the same data can also be used by an adversary to make
sensitive inferences about a user thereby violating her privacy.
  We present DEEProtect, a framework that enables a novel form of inference
control, in which mobile apps with access to sensor data are limited (provably)
in their ability to make inferences about user's sensitive data and behavior.
DEEProtect adopts a two-layered privacy strategy. First, it leverages novel
autoencoder techniques to perform data minimization and limits the amount of
information being shared; the learning network is used to derive a compact
representation of sensor data consisting only of features relevant to
authorized utility-providing inferences. Second, DEEProtect obfuscates the
previously learnt features, thereby providing an additional layer of protection
against sensitive inferences. Our framework supports both conventional as well
as a novel relaxed notion of local differential privacy that enhances utility.
Through theoretical analysis and extensive experiments using real-world
datasets, we demonstrate that when compared to existing approaches DEEProtect
provides provable privacy guarantees with up to 8x improvement in utility.
Finally, DEEProtect shares obfuscated but raw sensor data reconstructed from
the perturbed features, thus requiring no changes to the existing app
interfaces.
