Fusion of Mobile Device Signal Data Attributes Enables Multi-Protocol Entity Resolution and Enhanced Large-Scale Tracking
This work addresses privacy concerns for mobile device users by enabling multi-protocol tracking, though it is incremental as it builds on existing techniques.
The paper tackles the problem of tracking mobile devices across different wireless protocols using persistent identifiers, which poses a privacy risk, and introduces SEXTANT, a framework that achieves effective large-scale tracking with experiments on simulated data involving 300,000 devices and over 200 million observations.
Use of persistent identifiers in wireless communication protocols is a known privacy concern as they can be used to track the location of mobile devices. Furthermore, inherent structure in the assignment of hardware identifiers as well as upper-layer network protocol data attributes can leak additional device information. We introduce SEXTANT, a computational framework that combines improvements on previously published device identification techniques with novel spatio-temporal correlation algorithms to perform multi-protocol entity resolution, enabling large-scale tracking of mobile devices across protocol domains. Experiments using simulated data representing Las Vegas residents and visitors over a 30-day period, consisting of about 300,000 multi-protocol mobile devices generating over 200 million sensor observations, demonstrate SEXTANT's ability to perform effectively at scale while being robust to data heterogeneity, sparsity, and noise, highlighting the urgent need for the adoption of new standards to protect the privacy of mobile device users.