Platial k-Anonymity: Improving Location Anonymity Through Temporal Popularity Signatures

Authors Grant McKenzie , Hongyu Zhang

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Grant McKenzie
  • Platial Analysis Lab, McGill University, Montréal, Canada
Hongyu Zhang
  • Platial Analysis Lab, McGill University, Montréal, Canada

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Grant McKenzie and Hongyu Zhang. Platial k-Anonymity: Improving Location Anonymity Through Temporal Popularity Signatures. In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 9:1-9:15, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)


While it is increasingly necessary in today’s digital society, sharing personal location information comes at a cost. Sharing one’s precise place of interest, e.g., Compass Coffee, enables a range of location-based services, but substantially reduces the individual’s privacy. Methods have been developed to obfuscate and anonymize location data while still maintaining a degree of utility. One such approach, spatial k-anonymity, aims to ensure an individual’s level of anonymity by reporting their location as a set of k potential locations rather than their actual location alone. Larger values of k increase spatial anonymity while decreasing the utility of the location information. Typical examples of spatial k-anonymized datasets present elements as simple geographic points with no attributes or contextual information. In this work, we demonstrate that the addition of publicly available contextual data can significantly reduce the anonymity of a k-anonymized dataset. Through the analysis of place type temporal visitation patterns, hours of operation, and popularity values, one’s anonymity can be decreased by more than 50 percent. We propose a platial k-anonymity approach that leverages a combination of temporal popularity signatures and reports the amount that k must increase in order to maintain a certain level of anonymity. Finally, a method for reporting platial k-anonymous regions is presented and the implications of our methods are discussed.

Subject Classification

ACM Subject Classification
  • Security and privacy → Privacy protections
  • Information systems → Location based services
  • Information systems → Geographic information systems
  • location anonymity
  • location privacy
  • geoprivacy
  • place
  • temporal
  • geosocial


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