4.7 Article

Probabilistic positioning in mobile phone network and its consequences for the privacy of mobility data

期刊

出版社

ELSEVIER SCI LTD
DOI: 10.1016/j.compenvurbsys.2020.101550

关键词

Mobile phone positioning; Bayesian inference; Call details record; Location privacy

资金

  1. Blavatnik Interdisciplinary Cyber Research Center at Tel Aviv University
  2. Ministry of Science & Technology, Israel
  3. Ministry of Science & Technology of the People's Republic of China [3-15741]
  4. Israeli Ministry of Absorption

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The traditional method of mobile phone positioning relies on cell tower location as a proxy for device location, while our probabilistic approach is based on antenna parameters and connection numbers to address issues such as overlapping service areas and network load balancing. Through Bayesian inference, we are able to construct a more realistic distribution of device location, posing new challenges for mobile phone privacy and data analysis tools.
The traditional approach to mobile phone positioning is based on the assumption that the geographical location of a cell tower recorded in a Call Details Record (CDR) is a proxy for a device's location. A Voronoi tessellation is then constructed based on the entire network of cell towers and this tessellation is considered as a coordinate system, with the device located in a Vomnoi polygon of a cell tower that is recorded in the CDR. If Voronoi-based positioning is correct, the uniqueness of the device trajectory is very high, and the device can be identified based on 3-5 of its recorded locations. We investigate a probabilistic approach to device positioning that is based on knowledge of each antennas' parameters and number of connections, as dependent on the distance to the antenna. The critical difference between the Voronoi-based and the real world layout is in the essential overlap of the antennas' service areas: The device that is located in a cell tower's polygon can be served by a more distant antenna that is chosen by the network system to balance the network load. Combining data on the distance distribution of the number of connections available for each antenna in the network, we resolve the overlap problem by applying Bayesian inference and construct a realistic distribution of the device location. Probabilistic device positioning demands a full revision of mobile phone privacy and new full set of tools for data analysis.

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