@article{
 title = {Inferring Person-to-person Proximity Using WiFi Signals},
 type = {article},
 year = {2017},
 keywords = {privacy,proximity,social-sensing,wifi},
 volume = {1},
 websites = {https://dl.acm.org/citation.cfm?id=3090089},
 month = {6},
 id = {a4e191fa-1d30-3a40-a5d1-a660f8d3fc17},
 created = {2018-07-12T21:32:39.770Z},
 file_attached = {false},
 profile_id = {f954d000-ce94-3da6-bd26-b983145a920f},
 group_id = {b0b145a3-980e-3ad7-a16f-c93918c606ed},
 last_modified = {2018-07-12T21:32:39.770Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 citation_key = {Sapiezynski:WiFiProximity},
 source_type = {article},
 private_publication = {false},
 abstract = {Today's societies are enveloped in an ever-growing telecommunication infrastructure. This infrastructure offers important opportunities for sensing and recording a multitude of human behaviors. Human mobility patterns are a prominent example of such a behavior which has been studied based on cell phone towers, Bluetooth beacons, and WiFi networks as proxies for location. While mobility is an important aspect of human behavior, it is also crucial to study physical interactions among individuals. Sensing proximity that enables social interactions on a large scale is a technical challenge and many commonly used approaches—including RFID badges or Bluetooth scanning—offer only limited scalability. Here we show that it is possible, in a scalable and robust way, to accurately infer person-to-person physical proximity from the lists of WiFi access points measured by smartphones carried by the two individuals. Based on a longitudinal dataset of approximately 800 participants with ground-truth Bluetooth proximity collected over a year, we show that our model performs better than the current state-of-the-art. Our results demonstrate the value of WiFi signals as a tool for social sensing and show how collections of WiFi data pose a potential threat to privacy.},
 bibtype = {article},
 author = {Sapiezynski, Piotr and Stopczynski, Arkadiusz and Wind, David K and Leskovec, Jure and Lehmann, Sune},
 journal = {Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT)},
 number = {2}
}

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Based on a longitudinal dataset of approximately 800 participants with ground-truth Bluetooth proximity collected over a year, we show that our model performs better than the current state-of-the-art. 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