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How to Count Travelers Without Tracking Them Between Locations (Short Paper)

Authors Nadia Shafaeipour , Maarten van Steen , Frank O. Ostermann



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Author Details

Nadia Shafaeipour
  • Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, The Netherlands
Maarten van Steen
  • Digital Society Institute (DSI), University of Twente, Enschede, The Netherlands
Frank O. Ostermann
  • Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, The Netherlands

Cite AsGet BibTex

Nadia Shafaeipour, Maarten van Steen, and Frank O. Ostermann. How to Count Travelers Without Tracking Them Between Locations (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 66:1-66:6, Schloss Dagstuhl - Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.GIScience.2023.66

Abstract

Understanding the movements of travelers is essential for sustainable city planning, and unique identifiers from wireless network access points or smart card check-ins provide the necessary information to count and track individuals as they move between locations. Nevertheless, it is challenging to deal with such uniquely identifying data in a way that does not violate the privacy of individuals. Even though several protection techniques have been proposed, the data they produce can often still be used to track down specific individuals when combined with other external information. To address this issue, we use a novel method based on encrypted Bloom filters. These probabilistic data structures are used to represent sets while preserving privacy under strong cryptographic guarantees. In our setup, encrypted Bloom filters offer statistical counts of travelers as the only accessible information. However, the probabilistic nature of Bloom filters may lead to undercounting or overcounting of travelers, affecting accuracy. We explain our privacy-preserving method and examine the accuracy of counting the number of travelers as they move between locations. To accomplish this, we used a simulated subway dataset. The results indicate that it is possible to achieve highly accurate counting while ensuring that data cannot be used to trace and identify an individual.

Subject Classification

ACM Subject Classification
  • Security and privacy → Domain-specific security and privacy architectures
Keywords
  • Privacy preservation
  • encrypted Bloom filters
  • traveler counting
  • subway networks

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References

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