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Use of persistent identifiers in wireless communication protocols is a knownprivacy concern as they can be used to track the location of mobile devices.Furthermore, inherent structure in the assignment of hardware identifiers aswell as upper-layer network protocol data attributes can leak additional deviceinformation. We introduce SEXTANT, a computational framework that combinesimprovements on previously published device identification techniques withnovel spatio-temporal correlation algorithms to perform multi-protocol entityresolution, enabling large-scale tracking of mobile devices across protocoldomains. Experiments using simulated data representing Las Vegas residents andvisitors over a 30-day period, consisting of about 300,000 multi-protocolmobile devices generating over 200 million sensor observations, demonstrateSEXTANT's ability to perform effectively at scale while being robust to dataheterogeneity, sparsity, and noise, highlighting the urgent need for theadoption of new standards to protect the privacy of mobile device users.

Fusion of Mobile Device Signal Data Attributes Enables Multi-Protocol Entity Resolution and Enhanced Large-Scale Tracking