  Sharing location traces with context-aware service providers has privacy
implications. Location-privacy preserving mechanisms, such as obfuscation,
anonymization and cryptographic primitives, have been shown to have impractical
utility/privacy tradeoff. Another solution for enhancing user privacy is to
minimize data sharing by executing the tasks conventionally carried out at the
service providers' end on the users' smartphones. Although the data volume
shared with the untrusted entities is significantly reduced, executing
computationally demanding server-side tasks on resource-constrained smartphones
is often impracticable. To this end, we propose a novel perspective on lowering
the computational complexity by treating spatiotemporal trajectories as
space-time signals. Lowering the data dimensionality facilitates offloading the
computational tasks onto the digital-signal processors and the usage of the
non-blocking signal-processing pipelines. While focusing on the task of user
mobility modeling, we achieve the following results in comparison to the state
of the art techniques: (i) mobility models with precision and recall greater
than 80%, (ii) reduction in computational complexity by a factor of 2.5, and
(iii) reduction in power consumption by a factor of 0.5. Furthermore, our
technique does not rely on users' behavioral parameters that usually result in
privacy-leakage and conclusive bias in the existing techniques. Using three
real-world mobility datasets, we demonstrate that our technique addresses these
weaknesses while formulating accurate user mobility models.
