Capstone: Mobility Modeling on Smartphones to Achieve Privacy by Design

TL;DR

This paper introduces a novel approach to user mobility modeling on smartphones that reduces computational complexity and power consumption while enhancing privacy, by treating trajectories as space-time signals and leveraging signal processing techniques.

Contribution

The paper presents a new method for mobility modeling that minimizes data sharing and computational load on smartphones without relying on behavioral parameters.

Findings

Achieves mobility models with over 80% precision and recall.

Reduces computational complexity by a factor of 2.5.

Halves power consumption compared to existing methods.

Abstract

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.