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Sharing location traces with context-aware service providers has privacyimplications. Location-privacy preserving mechanisms, such as obfuscation,anonymization and cryptographic primitives, have been shown to have impracticalutility/privacy tradeoff. Another solution for enhancing user privacy is tominimize data sharing by executing the tasks conventionally carried out at theservice providers' end on the users' smartphones. Although the data volumeshared with the untrusted entities is significantly reduced, executingcomputationally demanding server-side tasks on resource-constrained smartphonesis often impracticable. To this end, we propose a novel perspective on loweringthe computational complexity by treating spatiotemporal trajectories asspace-time signals. Lowering the data dimensionality facilitates offloading thecomputational tasks onto the digital-signal processors and the usage of thenon-blocking signal-processing pipelines. While focusing on the task of usermobility modeling, we achieve the following results in comparison to the stateof the art techniques: (i) mobility models with precision and recall greaterthan 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, ourtechnique does not rely on users' behavioral parameters that usually result inprivacy-leakage and conclusive bias in the existing techniques. Using threereal-world mobility datasets, we demonstrate that our technique addresses theseweaknesses while formulating accurate user mobility models.

Capstone: Mobility Modeling on Smartphones to Achieve Privacy by Design